Methods and systems for reinforcement learning

Exemplary embodiments can maximize long-term value in a machine learning system. The system may employ an offline training process and an online training process. In the offline training process, an initial policy is learned to provide a warm start to the online training process. In the online training process, the system applies concurrent reinforcement learning across multiple environments, with the goal of learning efficient policies in real time from in-flight user data in one environment, and applying the learned policies to other environments. With the combination of offline training and online training, the system is able to improve initial performance through the warm start, while adapting to a changing context through concurrent reinforcement learning.

BACKGROUND

Exemplary embodiments described herein are directed to the problem of finding an efficient online control policy, in which a learner agent interacts with an environment. The learner agent may take actions in the environment and accrue some amount of value as a consequence of the actions. For example, in some implementations, the learner agent may employ a robotic device that is used to lift and transport heavy steel beams in an outdoor building environment, or a robotic arm that is used to repair parts on various machines in an industrial factory environment. The robotic device or arm may require maintenance on a periodic schedule, but experiences downtime while undergoing maintenance. On the other hand, if the robotic device/arm breaks due to lack of maintenance, a much longer downtime may be experienced. The goal of the agent may be to maximize the average long-term value accrued. In this example, the actions taking may include taking the robotic device/arm offline for maintenance, and the value may be represented as the long-term uptime of the robotic device/arm.

Often, such agents are embodied as a machine learning system, and may be trained using, for example, reinforcement learning. In reinforcement learning, a model simulates the dynamics of the environment with which the agent interacts. Accordingly, the agent can experiment with many different actions and identify some of the consequences of the actions. However, it is typically not possible to propagate a single interaction trajectory through time in order to learn a policy. In most real-world situations the model of the environment that the agent interacts with is not fully known a priori, because future dynamics cannot be predicted with complete accuracy. Maximizing the accrued value over time can be difficult in these circumstances.

These problems tend to be compounded when the system must deal with a changing context, such as when the value accrued depends on the actions or preferences of human actors (which can change over time and may not be entirely predictable). Existing reinforcement learning techniques generally require a significant amount of time and computer processing resources to adjust the model in response to a change in the environment. This may be disastrous when the environmental change requires quick action; it would be desirable for the system to change its actions within a few interactions and without waiting for the end of a predetermined time horizon to update its decision making.

Still further, the user preferences may not be monolithic. Different users may prefer different actions to different degrees and at different times, and hence the system must be particularly careful at each possible interaction point.

SUMMARY

This summary is not intended to identify only key or essential features of the described subject matter, nor is it intended to be used in isolation to determine the scope of the described subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification of this patent application, any or all drawings, and each claim.

In view of the above-described problems, it is desirable to utilize a model-free algorithm for online interactions. Model-free algorithms more readily adapt to changing environment dynamics than model-based algorithms. However, model-free algorithms have a problem in that an extensive exploration time is required before the algorithm can adopt a workable policy. In real-world scenarios, the consequences of such an extensive ramp-up time can be severe in terms of lost value and lost resources.

To overcome this issue, exemplary embodiments employ an online training stage in which an approximate model of the environment is used to initially train a reasonable starting policy. This starting policy is used to warm-start an offline stage, which relies on model-free algorithms (e.g., a Deep Concurrent Temporal Difference, or “DCTD,” algorithm). The model-free algorithm may interact with multiple different environments and may make changes in the policy based on the actions taken. Thus, consequences observed in one environment may be rapidly applied to change the policy before interacting with a second environment. Accordingly, the online algorithm can rapidly adapt to changing conditions.

Therefore, according to exemplary embodiments, methods, mediums, and systems are described for training an agent that selects from among a plurality of available actions to maximize an expected value over time.

In order to achieve this goal, a model of an environment may be built using historical information about the environment. The historical information about the environment may be represented a set of dynamics, where each dynamic includes: a current state of the environment; an action to be performed; a value associated with the action; and a next state of the environment to which the current state transitions after taking the action. Optionally, each dynamic may also include a set of next states and a probability of transitioning to each of the next states given that the action is taken.

The actions that may be taken in the environment may depend on the particular application. For instance, the actions may relate to predictive maintenance in a network, to inbound marketing, to outbound marketing, etc. Some examples include: taking devices offline for servicing; adding a new server to a virtual cluster (or removing a server from a virtual cluster); purchasing a book for library circulation; sending out an advertising campaign by mail, phone, electronic communication, etc. (or refraining from doing so for the present time); etc.

More generally, the action may be any act an agent is capable of taking at a given time. Accordingly, an environment may be associated with a list of possible actions, some of which may be available at any given time (and, in certain embodiments, some of which may not be available at all times). Each action is associated with the accrual of some amount of value, which may accrue immediately, in the future, or a combination. The value may depend on the application, and may be (for example): device uptime; network bandwidth; cost; patron satisfaction or retention; revenue; etc.

An initial policy may be trained using an offline process acting on the model of the environment. The policy may be represented by a number of policy weights that prioritize between available actions to identify a selected action. The offline training may include: (1) at each of multiple time steps, receiving the environment dynamics and storing the environment dynamics in a memory; (2) at each of the time steps, selecting an action from among available actions based on the policy weights; (3) repeating (1) and (2) until a final time step is reached; (4) at the final time step, determining a total value amount given the selected actions; (5) comparing the total value for different training sessions defined by different starting policy weights; and (6) selecting the policy weights from among the different policy weights based on which of the policy weights maximize the total value.

Using the initial policy as described herein may reduce a number of errors associated with applying the initial policy in the first live environment and increase the expected value over time as compared to a system in which the initial policy is not developed using the offline algorithm.

The initial policy from the offline process may be incorporated into an online process acting in a first live environment. The first live environment may be defined by an online stream of information describing present dynamics of the first live environment. The dynamics may represent a state of a real-world system and/or user interactions with the environment.

Within the first live environment, a number of possible actions that may be taken may be identified. The possible actions to be taken in the first live environment may be defined by the dynamics associated with a current state of the environment as defined by the online stream of information.

Using the online process, one of the actions may be selected based on the online process' evaluation of a value over time resulting from the actions in view of the present dynamics of the first live environment in view of the policy weights. A value associated with the selected action may be observed, and the online process may be retrained by updating the policy weights based on the observed value. Updating the policy weights may cause a reduction in a temporal difference error in the online process.

In some embodiments, the policy weights may be updated based on observation of multiple live environments at once. To this end, the agent may interact with multiple live environments in parallel by taking actions in respective environments during overlapping periods of interaction. The live environments may be divided into different training batches, and at least one live environment may be permitted to appear in multiple batches. Respective values for respective actions may be observed in each of the live environments. The observed values from each environment may be aggregated together, and the policy weights may be updated based on the aggregated value.

The online process may be configured to capture changes of dynamics in the environment caused by changing one or more of the reward for taking the action or the probability of transitioning to a next state given that the action is taken. In one embodiment, the online process may be a deep concurrent temporal difference (DCTD) algorithm that applies a deep neural network to a model-free reinforcement learning method.

The retrained online process may be applied via application logic in a second live environment defined by a different online stream of information defining different dynamics as compared to the first live environment.

Exemplary embodiments may be used for, among other things, predictive maintenance in a network of connected devices. For example, the above-described actions may involve taking a device offline for maintenance with the goal of reducing future downtime (e.g., due to system failures). The above-described value may be an uptime, bandwidth, or other usage metric for the network. Thus, by removing a device in the short-term (thereby potentially incurring a cost or penalty), the long-term usage of the network may be improved, although usage improvements are not guaranteed—for instance, the predictive maintenance may be unnecessary because the likelihood of device failure is low. By applying the combination of the offline and online training process, the system can make better decisions to improve long-term network usage.

Other envisioned applications include inbound marketing (where users approach a particular marketing entity, such as by visiting a website on which the entity's advertisements are present) and outbound marketing (where the marketing entity sends their marketing materials to potential customers). In either case, the offline training process allows the system to make better decisions regarding the marketing to be presented to particular users when initially deployed, while the online process allows the system to adapt to user's changing preferences over time.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of embodiments of the technology. However, it will be apparent that various embodiments may be practiced without these specific details. The figures and description are not intended to be restrictive. [0001] Systems depicted in some of the figures may be provided in various configurations. In some embodiments, the systems may be configured as a distributed system where one or more components of the system are distributed across one or more networks in a cloud computing system.

FIG. 1is a block diagram that provides an illustration of the hardware components of a data transmission network100, according to embodiments of the present technology. Data transmission network100is a specialized computer system that may be used for processing large amounts of data where a large number of computer processing cycles are required.

Data transmission network100may also include computing environment114. Computing environment114may be a specialized computer or other machine that processes the data received within the data transmission network100. Data transmission network100also includes one or more network devices102. Network devices102may include client devices that attempt to communicate with computing environment114. For example, network devices102may send data to the computing environment114to be processed, may send signals to the computing environment114to control different aspects of the computing environment or the data it is processing, among other reasons. Network devices102may interact with the computing environment114through a number of ways, such as, for example, over one or more networks108. As shown inFIG. 1, computing environment114may include one or more other systems. For example, computing environment114may include a database system118and/or a communications grid120.

In other embodiments, network devices may provide a large amount of data, either all at once or streaming over a period of time (e.g., using event stream processing (ESP), described further with respect toFIGS. 8-10), to the computing environment114via networks108. For example, network devices102may include network computers, sensors, databases, or other devices that may transmit or otherwise provide data to computing environment114. For example, network devices may include local area network devices, such as routers, hubs, switches, or other computer networking devices. These devices may provide a variety of stored or generated data, such as network data or data specific to the network devices themselves. Network devices may also include sensors that monitor their environment or other devices to collect data regarding that environment or those devices, and such network devices may provide data they collect over time. Network devices may also include devices within the internet of things, such as devices within a home automation network. Some of these devices may be referred to as edge devices, and may involve edge computing circuitry. Data may be transmitted by network devices directly to computing environment114or to network-attached data stores, such as network-attached data stores110for storage so that the data may be retrieved later by the computing environment114or other portions of data transmission network100.

Data transmission network100may also include one or more network-attached data stores110. Network-attached data stores110are used to store data to be processed by the computing environment114as well as any intermediate or final data generated by the computing system in non-volatile memory. However in certain embodiments, the configuration of the computing environment114allows its operations to be performed such that intermediate and final data results can be stored solely in volatile memory (e.g., RAM), without a requirement that intermediate or final data results be stored to non-volatile types of memory (e.g., disk). This can be useful in certain situations, such as when the computing environment114receives ad hoc queries from a user and when responses, which are generated by processing large amounts of data, need to be generated on-the-fly. In this non-limiting situation, the computing environment114may be configured to retain the processed information within memory so that responses can be generated for the user at different levels of detail as well as allow a user to interactively query against this information.

Network-attached data stores may store a variety of different types of data organized in a variety of different ways and from a variety of different sources. For example, network-attached data storage may include storage other than primary storage located within computing environment114that is directly accessible by processors located therein. Network-attached data storage may include secondary, tertiary or auxiliary storage, such as large hard drives, servers, virtual memory, among other types. Storage devices may include portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing data. A machine-readable storage medium or computer-readable storage medium may include a non-transitory medium in which data can be stored and that does not include carrier waves and/or transitory electronic signals. Examples of a non-transitory medium may include, for example, a magnetic disk or tape, optical storage media such as compact disk or digital versatile disk, flash memory, memory or memory devices. A computer-program product may include code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, among others. Furthermore, the data stores may hold a variety of different types of data. For example, network-attached data stores110may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales).

The unstructured data may be presented to the computing environment114in different forms such as a flat file or a conglomerate of data records, and may have data values and accompanying time stamps. The computing environment114may be used to analyze the unstructured data in a variety of ways to determine the best way to structure (e.g., hierarchically) that data, such that the structured data is tailored to a type of further analysis that a user wishes to perform on the data. For example, after being processed, the unstructured time stamped data may be aggregated by time (e.g., into daily time period units) to generate time series data and/or structured hierarchically according to one or more dimensions (e.g., parameters, attributes, and/or variables). For example, data may be stored in a hierarchical data structure, such as a ROLAP OR MOLAP database, or may be stored in another tabular form, such as in a flat-hierarchy form.

Data transmission network100may also include one or more server farms106. Computing environment114may route select communications or data to the one or more sever farms106or one or more servers within the server farms. Server farms106can be configured to provide information in a predetermined manner. For example, server farms106may access data to transmit in response to a communication. Server farms106may be separately housed from each other device within data transmission network100, such as computing environment114, and/or may be part of a device or system.

Server farms106may host a variety of different types of data processing as part of data transmission network100. Server farms106may receive a variety of different data from network devices, from computing environment114, from cloud network116, or from other sources. The data may have been obtained or collected from one or more sensors, as inputs from a control database, or may have been received as inputs from an external system or device. Server farms106may assist in processing the data by turning raw data into processed data based on one or more rules implemented by the server farms. For example, sensor data may be analyzed to determine changes in an environment over time or in real-time.

Data transmission network100may also include one or more cloud networks116. Cloud network116may include a cloud infrastructure system that provides cloud services. In certain embodiments, services provided by the cloud network116may include a host of services that are made available to users of the cloud infrastructure system on demand. Cloud network116is shown inFIG. 1as being connected to computing environment114(and therefore having computing environment114as its client or user), but cloud network116may be connected to or utilized by any of the devices inFIG. 1. Services provided by the cloud network can dynamically scale to meet the needs of its users. The cloud network116may comprise one or more computers, servers, and/or systems. In some embodiments, the computers, servers, and/or systems that make up the cloud network116are different from the user's own on-premises computers, servers, and/or systems. For example, the cloud network116may host an application, and a user may, via a communication network such as the Internet, on demand, order and use the application.

While each device, server and system inFIG. 1is shown as a single device, it will be appreciated that multiple devices may instead be used. For example, a set of network devices can be used to transmit various communications from a single user, or remote server140may include a server stack. As another example, data may be processed as part of computing environment114.

Each communication within data transmission network100(e.g., between client devices, between a device and connection management system150, between servers106and computing environment114or between a server and a device) may occur over one or more networks108. Networks108may include one or more of a variety of different types of networks, including a wireless network, a wired network, or a combination of a wired and wireless network. Examples of suitable networks include the Internet, a personal area network, a local area network (LAN), a wide area network (WAN), or a wireless local area network (WLAN). A wireless network may include a wireless interface or combination of wireless interfaces. As an example, a network in the one or more networks108may include a short-range communication channel, such as a Bluetooth or a Bluetooth Low Energy channel. A wired network may include a wired interface. The wired and/or wireless networks may be implemented using routers, access points, bridges, gateways, or the like, to connect devices in the network114, as will be further described with respect toFIG. 2. The one or more networks108can be incorporated entirely within or can include an intranet, an extranet, or a combination thereof. In one embodiment, communications between two or more systems and/or devices can be achieved by a secure communications protocol, such as secure sockets layer (SSL) or transport layer security (TLS). In addition, data and/or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things (e.g., machines, devices, phones, sensors) can be connected to networks and the data from these things can be collected and processed within the things and/or external to the things. For example, the IoT can include sensors in many different devices, and high value analytics can be applied to identify hidden relationships and drive increased efficiencies. This can apply to both big data analytics and real-time (e.g., ESP) analytics. IoT may be implemented in various areas, such as for access (technologies that get data and move it), embed-ability (devices with embedded sensors), and services. Industries in the IoT space may automotive (connected car), manufacturing (connected factory), smart cities, energy and retail. This will be described further below with respect toFIG. 2.

As noted, computing environment114may include a communications grid120and a transmission network database system118. Communications grid120may be a grid-based computing system for processing large amounts of data. The transmission network database system118may be for managing, storing, and retrieving large amounts of data that are distributed to and stored in the one or more network-attached data stores110or other data stores that reside at different locations within the transmission network database system118. The compute nodes in the grid-based computing system120and the transmission network database system118may share the same processor hardware, such as processors that are located within computing environment114.

FIG. 2illustrates an example network including an example set of devices communicating with each other over an exchange system and via a network, according to embodiments of the present technology. As noted, each communication within data transmission network100may occur over one or more networks. System200includes a network device204configured to communicate with a variety of types of client devices, for example client devices230, over a variety of types of communication channels.

As shown inFIG. 2, network device204can transmit a communication over a network (e.g., a cellular network via a base station210). The communication can be routed to another network device, such as network devices205-209, via base station210. The communication can also be routed to computing environment214via base station210. For example, network device204may collect data either from its surrounding environment or from other network devices (such as network devices205-209) and transmit that data to computing environment214.

Although network devices204-209are shown inFIG. 2as a mobile phone, laptop computer, tablet computer, temperature sensor, motion sensor, and audio sensor respectively, the network devices may be or include sensors that are sensitive to detecting aspects of their environment. For example, the network devices may include sensors such as water sensors, power sensors, electrical current sensors, chemical sensors, optical sensors, pressure sensors, geographic or position sensors (e.g., GPS), velocity sensors, acceleration sensors, flow rate sensors, among others. Examples of characteristics that may be sensed include force, torque, load, strain, position, temperature, air pressure, fluid flow, chemical properties, resistance, electromagnetic fields, radiation, irradiance, proximity, acoustics, moisture, distance, speed, vibrations, acceleration, electrical potential, electrical current, among others. The sensors may be mounted to various components used as part of a variety of different types of systems (e.g., an oil drilling operation). The network devices may detect and record data related to the environment that it monitors, and transmit that data to computing environment214.

As noted, one type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment according to certain embodiments includes an oil drilling system. For example, the one or more drilling operation sensors may include surface sensors that measure a hook load, a fluid rate, a temperature and a density in and out of the wellbore, a standpipe pressure, a surface torque, a rotation speed of a drill pipe, a rate of penetration, a mechanical specific energy, etc. and downhole sensors that measure a rotation speed of a bit, fluid densities, downhole torque, downhole vibration (axial, tangential, lateral), a weight applied at a drill bit, an annular pressure, a differential pressure, an azimuth, an inclination, a dog leg severity, a measured depth, a vertical depth, a downhole temperature, etc. Besides the raw data collected directly by the sensors, other data may include parameters either developed by the sensors or assigned to the system by a client or other controlling device. For example, one or more drilling operation control parameters may control settings such as a mud motor speed to flow ratio, a bit diameter, a predicted formation top, seismic data, weather data, etc. Other data may be generated using physical models such as an earth model, a weather model, a seismic model, a bottom hole assembly model, a well plan model, an annular friction model, etc. In addition to sensor and control settings, predicted outputs, of for example, the rate of penetration, mechanical specific energy, hook load, flow in fluid rate, flow out fluid rate, pump pressure, surface torque, rotation speed of the drill pipe, annular pressure, annular friction pressure, annular temperature, equivalent circulating density, etc. may also be stored in the data warehouse.

In another example, another type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment according to certain embodiments includes a home automation or similar automated network in a different environment, such as an office space, school, public space, sports venue, or a variety of other locations. Network devices in such an automated network may include network devices that allow a user to access, control, and/or configure various home appliances located within the user's home (e.g., a television, radio, light, fan, humidifier, sensor, microwave, iron, and/or the like), or outside of the user's home (e.g., exterior motion sensors, exterior lighting, garage door openers, sprinkler systems, or the like). For example, network device102may include a home automation switch that may be coupled with a home appliance. In another embodiment, a network device can allow a user to access, control, and/or configure devices, such as office-related devices (e.g., copy machine, printer, or fax machine), audio and/or video related devices (e.g., a receiver, a speaker, a projector, a DVD player, or a television), media-playback devices (e.g., a compact disc player, a CD player, or the like), computing devices (e.g., a home computer, a laptop computer, a tablet, a personal digital assistant (PDA), a computing device, or a wearable device), lighting devices (e.g., a lamp or recessed lighting), devices associated with a security system, devices associated with an alarm system, devices that can be operated in an automobile (e.g., radio devices, navigation devices), and/or the like. Data may be collected from such various sensors in raw form, or data may be processed by the sensors to create parameters or other data either developed by the sensors based on the raw data or assigned to the system by a client or other controlling device.

In another example, another type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment according to certain embodiments includes a power or energy grid. A variety of different network devices may be included in an energy grid, such as various devices within one or more power plants, energy farms (e.g., wind farm, solar farm, among others) energy storage facilities, factories, homes and businesses of consumers, among others. One or more of such devices may include one or more sensors that detect energy gain or loss, electrical input or output or loss, and a variety of other efficiencies. These sensors may collect data to inform users of how the energy grid, and individual devices within the grid, may be functioning and how they may be made more efficient.

Network device sensors may also perform processing on data it collects before transmitting the data to the computing environment114, or before deciding whether to transmit data to the computing environment114. For example, network devices may determine whether data collected meets certain rules, for example by comparing data or values computed from the data and comparing that data to one or more thresholds. The network device may use this data and/or comparisons to determine if the data should be transmitted to the computing environment214for further use or processing.

Computing environment214may include machines220and240. Although computing environment214is shown inFIG. 2as having two machines,220and240, computing environment214may have only one machine or may have more than two machines. The machines that make up computing environment214may include specialized computers, servers, or other machines that are configured to individually and/or collectively process large amounts of data. The computing environment214may also include storage devices that include one or more databases of structured data, such as data organized in one or more hierarchies, or unstructured data. The databases may communicate with the processing devices within computing environment214to distribute data to them. Since network devices may transmit data to computing environment214, that data may be received by the computing environment214and subsequently stored within those storage devices. Data used by computing environment214may also be stored in data stores235, which may also be a part of or connected to computing environment214.

Computing environment214can communicate with various devices via one or more routers225or other inter-network or intra-network connection components. For example, computing environment214may communicate with devices230via one or more routers225. Computing environment214may collect, analyze and/or store data from or pertaining to communications, client device operations, client rules, and/or user-associated actions stored at one or more data stores235. Such data may influence communication routing to the devices within computing environment214, how data is stored or processed within computing environment214, among other actions.

Notably, various other devices can further be used to influence communication routing and/or processing between devices within computing environment214and with devices outside of computing environment214. For example, as shown inFIG. 2, computing environment214may include a web server240. Thus, computing environment214can retrieve data of interest, such as client information (e.g., product information, client rules, etc.), technical product details, news, current or predicted weather, and so on.

In addition to computing environment214collecting data (e.g., as received from network devices, such as sensors, and client devices or other sources) to be processed as part of a big data analytics project, it may also receive data in real time as part of a streaming analytics environment. As noted, data may be collected using a variety of sources as communicated via different kinds of networks or locally. Such data may be received on a real-time streaming basis. For example, network devices may receive data periodically from network device sensors as the sensors continuously sense, monitor and track changes in their environments. Devices within computing environment214may also perform pre-analysis on data it receives to determine if the data received should be processed as part of an ongoing project. The data received and collected by computing environment214, no matter what the source or method or timing of receipt, may be processed over a period of time for a client to determine results data based on the client's needs and rules.

FIG. 3illustrates a representation of a conceptual model of a communications protocol system, according to embodiments of the present technology. More specifically,FIG. 3identifies operation of a computing environment in an Open Systems Interaction model that corresponds to various connection components. The model300shows, for example, how a computing environment, such as computing environment314(or computing environment214inFIG. 2) may communicate with other devices in its network, and control how communications between the computing environment and other devices are executed and under what conditions.

The model can include layers302-314. The layers are arranged in a stack. Each layer in the stack serves the layer one level higher than it (except for the application layer, which is the highest layer), and is served by the layer one level below it (except for the physical layer, which is the lowest layer). The physical layer is the lowest layer because it receives and transmits raw bites of data, and is the farthest layer from the user in a communications system. On the other hand, the application layer is the highest layer because it interacts directly with a software application.

As noted, the model includes a physical layer302. Physical layer302represents physical communication, and can define parameters of that physical communication. For example, such physical communication may come in the form of electrical, optical, or electromagnetic signals. Physical layer302also defines protocols that may control communications within a data transmission network.

Link layer304defines links and mechanisms used to transmit (i.e., move) data across a network. The link layer manages node-to-node communications, such as within a grid computing environment. Link layer304can detect and correct errors (e.g., transmission errors in the physical layer302). Link layer304can also include a media access control (MAC) layer and logical link control (LLC) layer.

Network layer306defines the protocol for routing within a network. In other words, the network layer coordinates transferring data across nodes in a same network (e.g., such as a grid computing environment). Network layer306can also define the processes used to structure local addressing within the network.

Transport layer308can manage the transmission of data and the quality of the transmission and/or receipt of that data. Transport layer308can provide a protocol for transferring data, such as, for example, a Transmission Control Protocol (TCP). Transport layer308can assemble and disassemble data frames for transmission. The transport layer can also detect transmission errors occurring in the layers below it.

Session layer310can establish, maintain, and manage communication connections between devices on a network. In other words, the session layer controls the dialogues or nature of communications between network devices on the network. The session layer may also establish checkpointing, adjournment, termination, and restart procedures.

Presentation layer312can provide translation for communications between the application and network layers. In other words, this layer may encrypt, decrypt and/or format data based on data types known to be accepted by an application or network layer.

Application layer314interacts directly with software applications and end users, and manages communications between them. Application layer314can identify destinations, local resource states or availability and/or communication content or formatting using the applications.

Intra-network connection components322and324are shown to operate in lower levels, such as physical layer302and link layer304, respectively. For example, a hub can operate in the physical layer, a switch can operate in the physical layer, and a router can operate in the network layer. Inter-network connection components326and328are shown to operate on higher levels, such as layers306-314. For example, routers can operate in the network layer and network devices can operate in the transport, session, presentation, and application layers.

As noted, a computing environment314can interact with and/or operate on, in various embodiments, one, more, all or any of the various layers. For example, computing environment314can interact with a hub (e.g., via the link layer) so as to adjust which devices the hub communicates with. The physical layer may be served by the link layer, so it may implement such data from the link layer. For example, the computing environment314may control which devices it will receive data from. For example, if the computing environment314knows that a certain network device has turned off, broken, or otherwise become unavailable or unreliable, the computing environment314may instruct the hub to prevent any data from being transmitted to the computing environment314from that network device. Such a process may be beneficial to avoid receiving data that is inaccurate or that has been influenced by an uncontrolled environment. As another example, computing environment314can communicate with a bridge, switch, router or gateway and influence which device within the system (e.g., system200) the component selects as a destination. In some embodiments, computing environment314can interact with various layers by exchanging communications with equipment operating on a particular layer by routing or modifying existing communications. In another embodiment, such as in a grid computing environment, a node may determine how data within the environment should be routed (e.g., which node should receive certain data) based on certain parameters or information provided by other layers within the model.

As noted, the computing environment314may be a part of a communications grid environment, the communications of which may be implemented as shown in the protocol ofFIG. 3. For example, referring back toFIG. 2, one or more of machines220and240may be part of a communications grid computing environment. A gridded computing environment may be employed in a distributed system with non-interactive workloads where data resides in memory on the machines, or compute nodes. In such an environment, analytic code, instead of a database management system, controls the processing performed by the nodes. Data is co-located by pre-distributing it to the grid nodes, and the analytic code on each node loads the local data into memory. Each node may be assigned a particular task such as a portion of a processing project, or to organize or control other nodes within the grid.

FIG. 4illustrates a communications grid computing system400including a variety of control and worker nodes, according to embodiments of the present technology. Communications grid computing system400includes three control nodes and one or more worker nodes. Communications grid computing system400includes control nodes402,404, and406. The control nodes are communicatively connected via communication paths451,453, and455. Therefore, the control nodes may transmit information (e.g., related to the communications grid or notifications), to and receive information from each other. Although communications grid computing system400is shown inFIG. 4as including three control nodes, the communications grid may include more or less than three control nodes.

Communications grid computing system (or just “communications grid”)400also includes one or more worker nodes. Shown inFIG. 4are six worker nodes410-420. AlthoughFIG. 4shows six worker nodes, a communications grid according to embodiments of the present technology may include more or less than six worker nodes. The number of worker nodes included in a communications grid may be dependent upon how large the project or data set is being processed by the communications grid, the capacity of each worker node, the time designated for the communications grid to complete the project, among others. Each worker node within the communications grid400may be connected (wired or wirelessly, and directly or indirectly) to control nodes402-406. Therefore, each worker node may receive information from the control nodes (e.g., an instruction to perform work on a project) and may transmit information to the control nodes (e.g., a result from work performed on a project). Furthermore, worker nodes may communicate with each other (either directly or indirectly). For example, worker nodes may transmit data between each other related to a job being performed or an individual task within a job being performed by that worker node. However, in certain embodiments, worker nodes may not, for example, be connected (communicatively or otherwise) to certain other worker nodes. In an embodiment, worker nodes may only be able to communicate with the control node that controls it, and may not be able to communicate with other worker nodes in the communications grid, whether they are other worker nodes controlled by the control node that controls the worker node, or worker nodes that are controlled by other control nodes in the communications grid.

A control node may connect with an external device with which the control node may communicate (e.g., a grid user, such as a server or computer, may connect to a controller of the grid). For example, a server or computer may connect to control nodes and may transmit a project or job to the node. The project may include a data set. The data set may be of any size. Once the control node receives such a project including a large data set, the control node may distribute the data set or projects related to the data set to be performed by worker nodes. Alternatively, for a project including a large data set, the data set may be receive or stored by a machine other than a control node (e.g., a Hadoop data node).

Control nodes may maintain knowledge of the status of the nodes in the grid (i.e., grid status information), accept work requests from clients, subdivide the work across worker nodes, coordinate the worker nodes, among other responsibilities. Worker nodes may accept work requests from a control node and provide the control node with results of the work performed by the worker node. A grid may be started from a single node (e.g., a machine, computer, server, etc.). This first node may be assigned or may start as the primary control node that will control any additional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or a controller of the grid) it may be assigned to a set of nodes. After the nodes are assigned to a project, a data structure (i.e., a communicator) may be created. The communicator may be used by the project for information to be shared between the project code running on each node. A communication handle may be created on each node. A handle, for example, is a reference to the communicator that is valid within a single process on a single node, and the handle may be used when requesting communications between nodes.

A control node, such as control node402, may be designated as the primary control node. A server, computer or other external device may connect to the primary control node. Once the control node receives a project, the primary control node may distribute portions of the project to its worker nodes for execution. For example, when a project is initiated on communications grid400, primary control node402controls the work to be performed for the project in order to complete the project as requested or instructed. The primary control node may distribute work to the worker nodes based on various factors, such as which subsets or portions of projects may be completed most efficiently and in the correct amount of time. For example, a worker node may perform analysis on a portion of data that is already local (e.g., stored on) the worker node. The primary control node also coordinates and processes the results of the work performed by each worker node after each worker node executes and completes its job. For example, the primary control node may receive a result from one or more worker nodes, and the control node may organize (e.g., collect and assemble) the results received and compile them to produce a complete result for the project received from the end user.

Any remaining control nodes, such as control nodes404and406, may be assigned as backup control nodes for the project. In an embodiment, backup control nodes may not control any portion of the project. Instead, backup control nodes may serve as a backup for the primary control node and take over as primary control node if the primary control node were to fail. If a communications grid were to include only a single control node, and the control node were to fail (e.g., the control node is shut off or breaks) then the communications grid as a whole may fail and any project or job being run on the communications grid may fail and may not complete. While the project may be run again, such a failure may cause a delay (severe delay in some cases, such as overnight delay) in completion of the project. Therefore, a grid with multiple control nodes, including a backup control node, may be beneficial.

To add another node or machine to the grid, the primary control node may open a pair of listening sockets, for example. A socket may be used to accept work requests from clients, and the second socket may be used to accept connections from other grid nodes). The primary control node may be provided with a list of other nodes (e.g., other machines, computers, servers) that will participate in the grid, and the role that each node will fill in the grid. Upon startup of the primary control node (e.g., the first node on the grid), the primary control node may use a network protocol to start the server process on every other node in the grid. Command line parameters, for example, may inform each node of one or more pieces of information, such as: the role that the node will have in the grid, the host name of the primary control node, the port number on which the primary control node is accepting connections from peer nodes, among others. The information may also be provided in a configuration file, transmitted over a secure shell tunnel, recovered from a configuration server, among others. While the other machines in the grid may not initially know about the configuration of the grid, that information may also be sent to each other node by the primary control node. Updates of the grid information may also be subsequently sent to those nodes.

For any control node other than the primary control node added to the grid, the control node may open three sockets. The first socket may accept work requests from clients, the second socket may accept connections from other grid members, and the third socket may connect (e.g., permanently) to the primary control node. When a control node (e.g., primary control node) receives a connection from another control node, it first checks to see if the peer node is in the list of configured nodes in the grid. If it is not on the list, the control node may clear the connection. If it is on the list, it may then attempt to authenticate the connection. If authentication is successful, the authenticating node may transmit information to its peer, such as the port number on which a node is listening for connections, the host name of the node, information about how to authenticate the node, among other information. When a node, such as the new control node, receives information about another active node, it will check to see if it already has a connection to that other node. If it does not have a connection to that node, it may then establish a connection to that control node.

Any worker node added to the grid may establish a connection to the primary control node and any other control nodes on the grid. After establishing the connection, it may authenticate itself to the grid (e.g., any control nodes, including both primary and backup, or a server or user controlling the grid). After successful authentication, the worker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is powered on or connected to an existing node on the grid or both), the node is assigned (e.g., by an operating system of the grid) a universally unique identifier (UUID). This unique identifier may help other nodes and external entities (devices, users, etc.) to identify the node and distinguish it from other nodes. When a node is connected to the grid, the node may share its unique identifier with the other nodes in the grid. Since each node may share its unique identifier, each node may know the unique identifier of every other node on the grid. Unique identifiers may also designate a hierarchy of each of the nodes (e.g., backup control nodes) within the grid. For example, the unique identifiers of each of the backup control nodes may be stored in a list of backup control nodes to indicate an order in which the backup control nodes will take over for a failed primary control node to become a new primary control node. However, a hierarchy of nodes may also be determined using methods other than using the unique identifiers of the nodes. For example, the hierarchy may be predetermined, or may be assigned based on other predetermined factors.

The grid may add new machines at any time (e.g., initiated from any control node). Upon adding a new node to the grid, the control node may first add the new node to its table of grid nodes. The control node may also then notify every other control node about the new node. The nodes receiving the notification may acknowledge that they have updated their configuration information.

Primary control node402may, for example, transmit one or more communications to backup control nodes404and406(and, for example, to other control or worker nodes within the communications grid). Such communications may sent periodically, at fixed time intervals, between known fixed stages of the project's execution, among other protocols. The communications transmitted by primary control node402may be of varied types and may include a variety of types of information. For example, primary control node402may transmit snapshots (e.g., status information) of the communications grid so that backup control node404always has a recent snapshot of the communications grid. The snapshot or grid status may include, for example, the structure of the grid (including, for example, the worker nodes in the grid, unique identifiers of the nodes, or their relationships with the primary control node) and the status of a project (including, for example, the status of each worker node's portion of the project). The snapshot may also include analysis or results received from worker nodes in the communications grid. The backup control nodes may receive and store the backup data received from the primary control node. The backup control nodes may transmit a request for such a snapshot (or other information) from the primary control node, or the primary control node may send such information periodically to the backup control nodes.

As noted, the backup data may allow the backup control node to take over as primary control node if the primary control node fails without requiring the grid to start the project over from scratch. If the primary control node fails, the backup control node that will take over as primary control node may retrieve the most recent version of the snapshot received from the primary control node and use the snapshot to continue the project from the stage of the project indicated by the backup data. This may prevent failure of the project as a whole.

A backup control node may use various methods to determine that the primary control node has failed. In one example of such a method, the primary control node may transmit (e.g., periodically) a communication to the backup control node that indicates that the primary control node is working and has not failed, such as a heartbeat communication. The backup control node may determine that the primary control node has failed if the backup control node has not received a heartbeat communication for a certain predetermined period of time. Alternatively, a backup control node may also receive a communication from the primary control node itself (before it failed) or from a worker node that the primary control node has failed, for example because the primary control node has failed to communicate with the worker node.

Different methods may be performed to determine which backup control node of a set of backup control nodes (e.g., backup control nodes404and406) will take over for failed primary control node402and become the new primary control node. For example, the new primary control node may be chosen based on a ranking or “hierarchy” of backup control nodes based on their unique identifiers. In an alternative embodiment, a backup control node may be assigned to be the new primary control node by another device in the communications grid or from an external device (e.g., a system infrastructure or an end user, such as a server or computer, controlling the communications grid). In another alternative embodiment, the backup control node that takes over as the new primary control node may be designated based on bandwidth or other statistics about the communications grid.

A worker node within the communications grid may also fail. If a worker node fails, work being performed by the failed worker node may be redistributed amongst the operational worker nodes. In an alternative embodiment, the primary control node may transmit a communication to each of the operable worker nodes still on the communications grid that each of the worker nodes should purposefully fail also. After each of the worker nodes fail, they may each retrieve their most recent saved checkpoint of their status and re-start the project from that checkpoint to minimize lost progress on the project being executed.

FIG. 5illustrates a flow chart showing an example process for adjusting a communications grid or a work project in a communications grid after a failure of a node, according to embodiments of the present technology. The process may include, for example, receiving grid status information including a project status of a portion of a project being executed by a node in the communications grid, as described in operation502. For example, a control node (e.g., a backup control node connected to a primary control node and a worker node on a communications grid) may receive grid status information, where the grid status information includes a project status of the primary control node or a project status of the worker node. The project status of the primary control node and the project status of the worker node may include a status of one or more portions of a project being executed by the primary and worker nodes in the communications grid. The process may also include storing the grid status information, as described in operation504. For example, a control node (e.g., a backup control node) may store the received grid status information locally within the control node. Alternatively, the grid status information may be sent to another device for storage where the control node may have access to the information.

The process may also include receiving a failure communication corresponding to a node in the communications grid in operation506. For example, a node may receive a failure communication including an indication that the primary control node has failed, prompting a backup control node to take over for the primary control node. In an alternative embodiment, a node may receive a failure that a worker node has failed, prompting a control node to reassign the work being performed by the worker node. The process may also include reassigning a node or a portion of the project being executed by the failed node, as described in operation508. For example, a control node may designate the backup control node as a new primary control node based on the failure communication upon receiving the failure communication. If the failed node is a worker node, a control node may identify a project status of the failed worker node using the snapshot of the communications grid, where the project status of the failed worker node includes a status of a portion of the project being executed by the failed worker node at the failure time.

The process may also include receiving updated grid status information based on the reassignment, as described in operation510, and transmitting a set of instructions based on the updated grid status information to one or more nodes in the communications grid, as described in operation512. The updated grid status information may include an updated project status of the primary control node or an updated project status of the worker node. The updated information may be transmitted to the other nodes in the grid to update their stale stored information.

FIG. 6illustrates a portion of a communications grid computing system600including a control node and a worker node, according to embodiments of the present technology. Communications grid600computing system includes one control node (control node602) and one worker node (worker node610) for purposes of illustration, but may include more worker and/or control nodes. The control node602is communicatively connected to worker node610via communication path650. Therefore, control node602may transmit information (e.g., related to the communications grid or notifications), to and receive information from worker node610via path650.

Similar to inFIG. 4, communications grid computing system (or just “communications grid”)600includes data processing nodes (control node602and worker node610). Nodes602and610comprise multi-core data processors. Each node602and610includes a grid-enabled software component (GESC)620that executes on the data processor associated with that node and interfaces with buffer memory622also associated with that node. Each node602and610includes a database management software (DBMS)628that executes on a database server (not shown) at control node602and on a database server (not shown) at worker node610.

Each node also includes a data store624. Data stores624, similar to network-attached data stores110inFIG. 1and data stores235inFIG. 2, are used to store data to be processed by the nodes in the computing environment. Data stores624may also store any intermediate or final data generated by the computing system after being processed, for example in non-volatile memory. However in certain embodiments, the configuration of the grid computing environment allows its operations to be performed such that intermediate and final data results can be stored solely in volatile memory (e.g., RAM), without a requirement that intermediate or final data results be stored to non-volatile types of memory. Storing such data in volatile memory may be useful in certain situations, such as when the grid receives queries (e.g., ad hoc) from a client and when responses, which are generated by processing large amounts of data, need to be generated quickly or on-the-fly. In such a situation, the grid may be configured to retain the data within memory so that responses can be generated at different levels of detail and so that a client may interactively query against this information.

Each node also includes a user-defined function (UDF)626. The UDF provides a mechanism for the DMBS628to transfer data to or receive data from the database stored in the data stores624that are managed by the DBMS. For example, UDF626can be invoked by the DBMS to provide data to the GESC for processing. The UDF626may establish a socket connection (not shown) with the GESC to transfer the data. Alternatively, the UDF626can transfer data to the GESC by writing data to shared memory accessible by both the UDF and the GESC.

The GESC620at the nodes602and620may be connected via a network, such as network108shown inFIG. 1. Therefore, nodes602and620can communicate with each other via the network using a predetermined communication protocol such as, for example, the Message Passing Interface (MPI). Each GESC620can engage in point-to-point communication with the GESC at another node or in collective communication with multiple GESCs via the network. The GESC620at each node may contain identical (or nearly identical) software instructions. Each node may be capable of operating as either a control node or a worker node. The GESC at the control node602can communicate, over a communication path652, with a client device630. More specifically, control node602may communicate with client application632hosted by the client device630to receive queries and to respond to those queries after processing large amounts of data.

DMBS628may control the creation, maintenance, and use of database or data structure (not shown) within a nodes602or610. The database may organize data stored in data stores624. The DMBS628at control node602may accept requests for data and transfer the appropriate data for the request. With such a process, collections of data may be distributed across multiple physical locations. In this example, each node602and610stores a portion of the total data managed by the management system in its associated data store624.

Furthermore, the DBMS may be responsible for protecting against data loss using replication techniques. Replication includes providing a backup copy of data stored on one node on one or more other nodes. Therefore, if one node fails, the data from the failed node can be recovered from a replicated copy residing at another node. However, as described herein with respect toFIG. 4, data or status information for each node in the communications grid may also be shared with each node on the grid.

FIG. 7illustrates a flow chart showing an example method for executing a project within a grid computing system, according to embodiments of the present technology. As described with respect toFIG. 6, the GESC at the control node may transmit data with a client device (e.g., client device630) to receive queries for executing a project and to respond to those queries after large amounts of data have been processed. The query may be transmitted to the control node, where the query may include a request for executing a project, as described in operation702. The query can contain instructions on the type of data analysis to be performed in the project and whether the project should be executed using the grid-based computing environment, as shown in operation704.

To initiate the project, the control node may determine if the query requests use of the grid-based computing environment to execute the project. If the determination is no, then the control node initiates execution of the project in a solo environment (e.g., at the control node), as described in operation710. If the determination is yes, the control node may initiate execution of the project in the grid-based computing environment, as described in operation706. In such a situation, the request may include a requested configuration of the grid. For example, the request may include a number of control nodes and a number of worker nodes to be used in the grid when executing the project. After the project has been completed, the control node may transmit results of the analysis yielded by the grid, as described in operation708. Whether the project is executed in a solo or grid-based environment, the control node provides the results of the project.

As noted with respect toFIG. 2, the computing environments described herein may collect data (e.g., as received from network devices, such as sensors, such as network devices204-209inFIG. 2, and client devices or other sources) to be processed as part of a data analytics project, and data may be received in real time as part of a streaming analytics environment (e.g., ESP). Data may be collected using a variety of sources as communicated via different kinds of networks or locally, such as on a real-time streaming basis. For example, network devices may receive data periodically from network device sensors as the sensors continuously sense, monitor and track changes in their environments. More specifically, an increasing number of distributed applications develop or produce continuously flowing data from distributed sources by applying queries to the data before distributing the data to geographically distributed recipients. An event stream processing engine (ESPE) may continuously apply the queries to the data as it is received and determines which entities should receive the data. Client or other devices may also subscribe to the ESPE or other devices processing ESP data so that they can receive data after processing, based on for example the entities determined by the processing engine. For example, client devices230inFIG. 2may subscribe to the ESPE in computing environment214. In another example, event subscription devices1024a-c, described further with respect toFIG. 10, may also subscribe to the ESPE. The ESPE may determine or define how input data or event streams from network devices or other publishers (e.g., network devices204-209inFIG. 2) are transformed into meaningful output data to be consumed by subscribers, such as for example client devices230inFIG. 2.

FIG. 8illustrates a block diagram including components of an Event Stream Processing Engine (ESPE), according to embodiments of the present technology. ESPE800may include one or more projects802. A project may be described as a second-level container in an engine model managed by ESPE800where a thread pool size for the project may be defined by a user. Each project of the one or more projects802may include one or more continuous queries804that contain data flows, which are data transformations of incoming event streams. The one or more continuous queries804may include one or more source windows806and one or more derived windows808.

The ESPE may receive streaming data over a period of time related to certain events, such as events or other data sensed by one or more network devices. The ESPE may perform operations associated with processing data created by the one or more devices. For example, the ESPE may receive data from the one or more network devices204-209shown inFIG. 2. As noted, the network devices may include sensors that sense different aspects of their environments, and may collect data over time based on those sensed observations. For example, the ESPE may be implemented within one or more of machines220and240shown inFIG. 2. The ESPE may be implemented within such a machine by an ESP application. An ESP application may embed an ESPE with its own dedicated thread pool or pools into its application space where the main application thread can do application-specific work and the ESPE processes event streams at least by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that manages the resources of the one or more projects802. In an illustrative embodiment, for example, there may be only one ESPE800for each instance of the ESP application, and ESPE800may have a unique engine name. Additionally, the one or more projects802may each have unique project names, and each query may have a unique continuous query name and begin with a uniquely named source window of the one or more source windows806. ESPE800may or may not be persistent.

Continuous query modeling involves defining directed graphs of windows for event stream manipulation and transformation. A window in the context of event stream manipulation and transformation is a processing node in an event stream processing model. A window in a continuous query can perform aggregations, computations, pattern-matching, and other operations on data flowing through the window. A continuous query may be described as a directed graph of source, relational, pattern matching, and procedural windows. The one or more source windows806and the one or more derived windows808represent continuously executing queries that generate updates to a query result set as new event blocks stream through ESPE800. A directed graph, for example, is a set of nodes connected by edges, where the edges have a direction associated with them.

An event object may be described as a packet of data accessible as a collection of fields, with at least one of the fields defined as a key or unique identifier (ID). The event object may be created using a variety of formats including binary, alphanumeric, XML, etc. Each event object may include one or more fields designated as a primary identifier (ID) for the event so ESPE800can support operation codes (opcodes) for events including insert, update, upsert, and delete. Upsert opcodes update the event if the key field already exists; otherwise, the event is inserted. For illustration, an event object may be a packed binary representation of a set of field values and include both metadata and field data associated with an event. The metadata may include an opcode indicating if the event represents an insert, update, delete, or upsert, a set of flags indicating if the event is a normal, partial-update, or a retention generated event from retention policy management, and a set of microsecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of event objects. An event stream may be described as a flow of event block objects. A continuous query of the one or more continuous queries804transforms a source event stream made up of streaming event block objects published into ESPE800into one or more output event streams using the one or more source windows806and the one or more derived windows808. A continuous query can also be thought of as data flow modeling.

The one or more source windows806are at the top of the directed graph and have no windows feeding into them. Event streams are published into the one or more source windows806, and from there, the event streams may be directed to the next set of connected windows as defined by the directed graph. The one or more derived windows808are all instantiated windows that are not source windows and that have other windows streaming events into them. The one or more derived windows808may perform computations or transformations on the incoming event streams. The one or more derived windows808transform event streams based on the window type (that is operators such as join, filter, compute, aggregate, copy, pattern match, procedural, union, etc.) and window settings. As event streams are published into ESPE800, they are continuously queried, and the resulting sets of derived windows in these queries are continuously updated.

FIG. 9illustrates a flow chart showing an example process including operations performed by an event stream processing engine, according to some embodiments of the present technology. As noted, the ESPE800(or an associated ESP application) defines how input event streams are transformed into meaningful output event streams. More specifically, the ESP application may define how input event streams from publishers (e.g., network devices providing sensed data) are transformed into meaningful output event streams consumed by subscribers (e.g., a data analytics project being executed by a machine or set of machines).

Within the application, a user may interact with one or more user interface windows presented to the user in a display under control of the ESPE independently or through a browser application in an order selectable by the user. For example, a user may execute an ESP application, which causes presentation of a first user interface window, which may include a plurality of menus and selectors such as drop down menus, buttons, text boxes, hyperlinks, etc. associated with the ESP application as understood by a person of skill in the art. As further understood by a person of skill in the art, various operations may be performed in parallel, for example, using a plurality of threads.

At operation900, an ESP application may define and start an ESPE, thereby instantiating an ESPE at a device, such as machine220and/or240. In an operation902, the engine container is created. For illustration, ESPE800may be instantiated using a function call that specifies the engine container as a manager for the model.

In an operation904, the one or more continuous queries804are instantiated by ESPE800as a model. The one or more continuous queries804may be instantiated with a dedicated thread pool or pools that generate updates as new events stream through ESPE800. For illustration, the one or more continuous queries804may be created to model business processing logic within ESPE800, to predict events within ESPE800, to model a physical system within ESPE800, to predict the physical system state within ESPE800, etc. For example, as noted, ESPE800may be used to support sensor data monitoring and management (e.g., sensing may include force, torque, load, strain, position, temperature, air pressure, fluid flow, chemical properties, resistance, electromagnetic fields, radiation, irradiance, proximity, acoustics, moisture, distance, speed, vibrations, acceleration, electrical potential, or electrical current, etc.).

ESPE800may analyze and process events in motion or “event streams.” Instead of storing data and running queries against the stored data, ESPE800may store queries and stream data through them to allow continuous analysis of data as it is received. The one or more source windows806and the one or more derived windows808may be created based on the relational, pattern matching, and procedural algorithms that transform the input event streams into the output event streams to model, simulate, score, test, predict, etc. based on the continuous query model defined and application to the streamed data.

In an operation906, a publish/subscribe (pub/sub) capability is initialized for ESPE800. In an illustrative embodiment, a pub/sub capability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE800, a port number may be provided. Pub/sub clients can use a host name of an ESP device running the ESPE and the port number to establish pub/sub connections to ESPE800.

FIG. 10illustrates an ESP system1000interfacing between publishing device1022and event subscribing devices1024a-c, according to embodiments of the present technology. ESP system1000may include ESP device or subsystem1001, event publishing device1022, an event subscribing device A1024a, an event subscribing device B1024b, and an event subscribing device C1024c. Input event streams are output to ESP device1001by publishing device1022. In alternative embodiments, the input event streams may be created by a plurality of publishing devices. The plurality of publishing devices further may publish event streams to other ESP devices. The one or more continuous queries instantiated by ESPE800may analyze and process the input event streams to form output event streams output to event subscribing device A1024a, event subscribing device B1024b, and event subscribing device C1024c. ESP system1000may include a greater or a fewer number of event subscribing devices of event subscribing devices.

Publish-subscribe is a message-oriented interaction paradigm based on indirect addressing. Processed data recipients specify their interest in receiving information from ESPE800by subscribing to specific classes of events, while information sources publish events to ESPE800without directly addressing the receiving parties. ESPE800coordinates the interactions and processes the data. In some cases, the data source receives confirmation that the published information has been received by a data recipient.

A publish/subscribe API may be described as a library that enables an event publisher, such as publishing device1022, to publish event streams into ESPE800or an event subscriber, such as event subscribing device A1024a, event subscribing device B1024b, and event subscribing device C1024c, to subscribe to event streams from ESPE800. For illustration, one or more publish/subscribe APIs may be defined. Using the publish/subscribe API, an event publishing application may publish event streams into a running event stream processor project source window of ESPE800, and the event subscription application may subscribe to an event stream processor project source window of ESPE800.

The publish/subscribe API provides cross-platform connectivity and endianness compatibility between ESP application and other networked applications, such as event publishing applications instantiated at publishing device1022, and event subscription applications instantiated at one or more of event subscribing device A1024a, event subscribing device B1024b, and event subscribing device C1024c.

Referring back toFIG. 9, operation906initializes the publish/subscribe capability of ESPE800. In an operation908, the one or more projects802are started. The one or more started projects may run in the background on an ESP device. In an operation910, an event block object is received from one or more computing device of the event publishing device1022.

ESP subsystem800may include a publishing client1002, ESPE800, a subscribing client A1004, a subscribing client B1006, and a subscribing client C1008. Publishing client1002may be started by an event publishing application executing at publishing device1022using the publish/subscribe API. Subscribing client A1004may be started by an event subscription application A, executing at event subscribing device A1024ausing the publish/subscribe API. Subscribing client B1006may be started by an event subscription application B executing at event subscribing device B1024busing the publish/subscribe API. Subscribing client C1008may be started by an event subscription application C executing at event subscribing device C1024cusing the publish/subscribe API.

An event block object containing one or more event objects is injected into a source window of the one or more source windows806from an instance of an event publishing application on event publishing device1022. The event block object may be generated, for example, by the event publishing application and may be received by publishing client1002. A unique ID may be maintained as the event block object is passed between the one or more source windows806and/or the one or more derived windows808of ESPE800, and to subscribing client A1004, subscribing client B806, and subscribing client C808and to event subscription device A1024a, event subscription device B1024b, and event subscription device C1024c. Publishing client1002may further generate and include a unique embedded transaction ID in the event block object as the event block object is processed by a continuous query, as well as the unique ID that publishing device1022assigned to the event block object.

In an operation912, the event block object is processed through the one or more continuous queries804. In an operation914, the processed event block object is output to one or more computing devices of the event subscribing devices1024a-c. For example, subscribing client A804, subscribing client B806, and subscribing client C808may send the received event block object to event subscription device A1024a, event subscription device B1024b, and event subscription device C1024c, respectively.

ESPE800maintains the event block containership aspect of the received event blocks from when the event block is published into a source window and works its way through the directed graph defined by the one or more continuous queries804with the various event translations before being output to subscribers. Subscribers can correlate a group of subscribed events back to a group of published events by comparing the unique ID of the event block object that a publisher, such as publishing device1022, attached to the event block object with the event block ID received by the subscriber.

In an operation916, a determination is made concerning whether or not processing is stopped. If processing is not stopped, processing continues in operation910to continue receiving the one or more event streams containing event block objects from the, for example, one or more network devices. If processing is stopped, processing continues in an operation918. In operation918, the started projects are stopped. In operation920, the ESPE is shutdown.

As noted, in some embodiments, big data is processed for an analytics project after the data is received and stored. In other embodiments, distributed applications process continuously flowing data in real-time from distributed sources by applying queries to the data before distributing the data to geographically distributed recipients. As noted, an event stream processing engine (ESPE) may continuously apply the queries to the data as it is received and determines which entities receive the processed data. This allows for large amounts of data being received and/or collected in a variety of environments to be processed and distributed in real time. For example, as shown with respect toFIG. 2, data may be collected from network devices that may include devices within the internet of things, such as devices within a home automation network. However, such data may be collected from a variety of different resources in a variety of different environments. In any such situation, embodiments of the present technology allow for real-time processing of such data.

Aspects of the current disclosure provide technical solutions to technical problems, such as computing problems that arise when an ESP device fails which results in a complete service interruption and potentially significant data loss. The data loss can be catastrophic when the streamed data is supporting mission critical operations such as those in support of an ongoing manufacturing or drilling operation. An embodiment of an ESP system achieves a rapid and seamless failover of ESPE running at the plurality of ESP devices without service interruption or data loss, thus significantly improving the reliability of an operational system that relies on the live or real-time processing of the data streams. The event publishing systems, the event subscribing systems, and each ESPE not executing at a failed ESP device are not aware of or effected by the failed ESP device. The ESP system may include thousands of event publishing systems and event subscribing systems. The ESP system keeps the failover logic and awareness within the boundaries of out-messaging network connector and out-messaging network device.

In one example embodiment, a system is provided to support a failover when event stream processing (ESP) event blocks. The system includes, but is not limited to, an out-messaging network device and a computing device. The computing device includes, but is not limited to, a processor and a computer-readable medium operably coupled to the processor. The processor is configured to execute an ESP engine (ESPE). The computer-readable medium has instructions stored thereon that, when executed by the processor, cause the computing device to support the failover. An event block object is received from the ESPE that includes a unique identifier. A first status of the computing device as active or standby is determined. When the first status is active, a second status of the computing device as newly active or not newly active is determined. Newly active is determined when the computing device is switched from a standby status to an active status. When the second status is newly active, a last published event block object identifier that uniquely identifies a last published event block object is determined. A next event block object is selected from a non-transitory computer-readable medium accessible by the computing device. The next event block object has an event block object identifier that is greater than the determined last published event block object identifier. The selected next event block object is published to an out-messaging network device. When the second status of the computing device is not newly active, the received event block object is published to the out-messaging network device. When the first status of the computing device is standby, the received event block object is stored in the non-transitory computer-readable medium.

FIG. 11is a flow chart of an example of a process for generating and using a machine-learning model according to some aspects. Machine learning is a branch of artificial intelligence that relates to mathematical models that can learn from, categorize, and make predictions about data. Such mathematical models, which can be referred to as machine-learning models, can classify input data among two or more classes; cluster input data among two or more groups; predict a result based on input data; identify patterns or trends in input data; identify a distribution of input data in a space; or any combination of these. Examples of machine-learning models can include (i) neural networks; (ii) decision trees, such as classification trees and regression trees; (iii) classifiers, such as Naïve bias classifiers, logistic regression classifiers, ridge regression classifiers, random forest classifiers, least absolute shrinkage and selector (LASSO) classifiers, and support vector machines; (iv) clusterers, such as k-means clusterers, mean-shift clusterers, and spectral clusterers; (v) factorizers, such as factorization machines, principal component analyzers and kernel principal component analyzers; and (vi) ensembles or other combinations of machine-learning models. In some examples, neural networks can include deep neural networks, feed-forward neural networks, recurrent neural networks, convolutional neural networks, radial basis function (RBF) neural networks, echo state neural networks, long short-term memory neural networks, bi-directional recurrent neural networks, gated neural networks, hierarchical recurrent neural networks, stochastic neural networks, modular neural networks, spiking neural networks, dynamic neural networks, cascading neural networks, neuro-fuzzy neural networks, or any combination of these.

Different machine-learning models may be used interchangeably to perform a task. Examples of tasks that can be performed at least partially using machine-learning models include various types of scoring; bioinformatics; cheminformatics; software engineering; fraud detection; customer segmentation; generating online recommendations; adaptive websites; determining customer lifetime value; search engines; placing advertisements in real time or near real time; classifying DNA sequences; affective computing; performing natural language processing and understanding; object recognition and computer vision; robotic locomotion; playing games; optimization and metaheuristics; detecting network intrusions; medical diagnosis and monitoring; or predicting when an asset, such as a machine, will need maintenance.

Any number and combination of tools can be used to create machine-learning models. Examples of tools for creating and managing machine-learning models can include SAS® Enterprise Miner, SAS® Rapid Predictive Modeler, and SAS® Model Manager, SAS Cloud Analytic Services (CAS)®, SAS Viya® of all which are by SAS Institute Inc. of Cary, N.C.

Machine-learning models can be constructed through an at least partially automated (e.g., with little or no human involvement) process called training. During training, input data can be iteratively supplied to a machine-learning model to enable the machine-learning model to identify patterns related to the input data or to identify relationships between the input data and output data. With training, the machine-learning model can be transformed from an untrained state to a trained state. Input data can be split into one or more training sets and one or more validation sets, and the training process may be repeated multiple times. The splitting may follow a k-fold cross-validation rule, a leave-one-out-rule, a leave-p-out rule, or a holdout rule. An overview of training and using a machine-learning model is described below with respect to the flow chart ofFIG. 11.

In block1104, training data is received. In some examples, the training data is received from a remote database or a local database, constructed from various subsets of data, or input by a user. The training data can be used in its raw form for training a machine-learning model or pre-processed into another form, which can then be used for training the machine-learning model. For example, the raw form of the training data can be smoothed, truncated, aggregated, clustered, or otherwise manipulated into another form, which can then be used for training the machine-learning model.

In block1106, a machine-learning model is trained using the training data. The machine-learning model can be trained in a supervised, unsupervised, or semi-supervised manner. In supervised training, each input in the training data is correlated to a desired output. This desired output may be a scalar, a vector, or a different type of data structure such as text or an image. This may enable the machine-learning model to learn a mapping between the inputs and desired outputs. In unsupervised training, the training data includes inputs, but not desired outputs, so that the machine-learning model has to find structure in the inputs on its own. In semi-supervised training, only some of the inputs in the training data are correlated to desired outputs.

In block1108, the machine-learning model is evaluated. For example, an evaluation dataset can be obtained, for example, via user input or from a database. The evaluation dataset can include inputs correlated to desired outputs. The inputs can be provided to the machine-learning model and the outputs from the machine-learning model can be compared to the desired outputs. If the outputs from the machine-learning model closely correspond with the desired outputs, the machine-learning model may have a high degree of accuracy. For example, if 90% or more of the outputs from the machine-learning model are the same as the desired outputs in the evaluation dataset, the machine-learning model may have a high degree of accuracy. Otherwise, the machine-learning model may have a low degree of accuracy. The 90% number is an example only. A realistic and desirable accuracy percentage is dependent on the problem and the data.

In some examples, if the machine-learning model has an inadequate degree of accuracy for a particular task, the process can return to block1106, where the machine-learning model can be further trained using additional training data or otherwise modified to improve accuracy. If the machine-learning model has an adequate degree of accuracy for the particular task, the process can continue to block1110.

In block1110, new data is received. In some examples, the new data is received from a remote database or a local database, constructed from various subsets of data, or input by a user. The new data may be unknown to the machine-learning model. For example, the machine-learning model may not have previously processed or analyzed the new data.

In block1112, the trained machine-learning model is used to analyze the new data and provide a result. For example, the new data can be provided as input to the trained machine-learning model. The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these.

In block1114, the result is post-processed. For example, the result can be added to, multiplied with, or otherwise combined with other data as part of a job. As another example, the result can be transformed from a first format, such as a time series format, into another format, such as a count series format. Any number and combination of operations can be performed on the result during post-processing.

A more specific example of a machine-learning model is the neural network1200shown inFIG. 12. The neural network1200is represented as multiple layers of interconnected neurons, such as neuron1208, that can exchange data between one another. The layers include an input layer1202for receiving input data, a hidden layer1204, and an output layer1206for providing a result. The hidden layer1204is referred to as hidden because it may not be directly observable or have its input directly accessible during the normal functioning of the neural network1200. Although the neural network1200is shown as having a specific number of layers and neurons for exemplary purposes, the neural network1200can have any number and combination of layers, and each layer can have any number and combination of neurons.

The neurons and connections between the neurons can have numeric weights, which can be tuned during training. For example, training data can be provided to the input layer1202of the neural network1200, and the neural network1200can use the training data to tune one or more numeric weights of the neural network1200. In some examples, the neural network1200can be trained using backpropagation. Backpropagation can include determining a gradient of a particular numeric weight based on a difference between an actual output of the neural network1200and a desired output of the neural network1200. Based on the gradient, one or more numeric weights of the neural network1200can be updated to reduce the difference, thereby increasing the accuracy of the neural network1200. This process can be repeated multiple times to train the neural network1200. For example, this process can be repeated hundreds or thousands of times to train the neural network1200.

In some examples, the neural network1200is a feed-forward neural network. In a feed-forward neural network, every neuron only propagates an output value to a subsequent layer of the neural network1200. For example, data may only move one direction (forward) from one neuron to the next neuron in a feed-forward neural network.

In other examples, the neural network1200is a recurrent neural network. A recurrent neural network can include one or more feedback loops, allowing data to propagate in both forward and backward through the neural network1200. This can allow for information to persist within the recurrent neural network. For example, a recurrent neural network can determine an output based at least partially on information that the recurrent neural network has seen before, giving the recurrent neural network the ability to use previous input to inform the output.

In some examples, the neural network1200operates by receiving a vector of numbers from one layer; transforming the vector of numbers into a new vector of numbers using a matrix of numeric weights, a nonlinearity, or both; and providing the new vector of numbers to a subsequent layer of the neural network1200. Each subsequent layer of the neural network1200can repeat this process until the neural network1200outputs a final result at the output layer1206. For example, the neural network1200can receive a vector of numbers as an input at the input layer1202. The neural network1200can multiply the vector of numbers by a matrix of numeric weights to determine a weighted vector. The matrix of numeric weights can be tuned during the training of the neural network1200. The neural network1200can transform the weighted vector using a nonlinearity, such as a sigmoid tangent or the hyperbolic tangent. In some examples, the nonlinearity can include a rectified linear unit, which can be expressed using the following equation:
y=max(x,0)
where y is the output and x is an input value from the weighted vector. The transformed output can be supplied to a subsequent layer, such as the hidden layer1204, of the neural network1200. The subsequent layer of the neural network1200can receive the transformed output, multiply the transformed output by a matrix of numeric weights and a nonlinearity, and provide the result to yet another layer of the neural network1200. This process continues until the neural network1200outputs a final result at the output layer1206.

Other examples of the present disclosure may include any number and combination of machine-learning models having any number and combination of characteristics. The machine-learning model(s) can be trained in a supervised, semi-supervised, or unsupervised manner, or any combination of these. The machine-learning model(s) can be implemented using a single computing device or multiple computing devices, such as the communications grid computing system400discussed above.

Implementing some examples of the present disclosure at least in part by using machine-learning models can reduce the total number of processing iterations, time, memory, electrical power, or any combination of these consumed by a computing device when analyzing data. For example, a neural network may more readily identify patterns in data than other approaches. This may enable the neural network to analyze the data using fewer processing cycles and less memory than other approaches, while obtaining a similar or greater level of accuracy.

Some machine-learning approaches may be more efficiently and speedily executed and processed with machine-learning specific processors (e.g., not a generic CPU). Such processors may also provide an energy savings when compared to generic CPUs. For example, some of these processors can include a graphical processing unit (GPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), an artificial intelligence (AI) accelerator, a neural computing core, a neural computing engine, a neural processing unit, a purpose-built chip architecture for deep learning, and/or some other machine-learning specific processor that implements a machine learning approach or one or more neural networks using semiconductor (e.g., silicon (Si), gallium arsenide (GaAs)) devices. Furthermore, these processors may also be employed in heterogeneous computing architectures with a number of and a variety of different types of cores, engines, nodes, and/or layers to achieve various energy efficiencies, processing speed improvements, processor(s) thermal mitigations, data communication speed improvements, and/or data efficiency targets and improvements throughout various parts of the system when compared to a homogeneous computing architecture that employs CPUs for general purpose computing.

Value Optimization

Exemplary embodiments provide a framework to address optimization problems (of which one example is the “customer journey problem”) using reinforcement learning and neural networks. An objective is to train a policy, which chooses among available actions to maximize a long-term value. By following a properly-selected policy, the agent has the opportunity to influence the users' future preferences and effectively change their reactions in the next interaction points, motivating them to continue accruing additional value. To meet these requirements, a dynamically learning, interactive algorithm is needed, which keeps track of user reactions and adapts accordingly to users' behavioral changes.

Many machine learning algorithms are designed to be trained in supervised fashion. However, supervised learning is not applicable where, as in the contemplated applications of the present technology, the supervisors do not have information about the true long-term value to use for training labels.

An alternative to supervised learning is an offline reinforcement learning (RL) algorithm. RL algorithms rely on models that simulate environment transitions. The learning agent takes actions according to its own policy and then adjusts its policy with respect to the reward revived from the model. After a good policy is achieved it is deployed for use with real user interactions.

However, there are still challenges in a live environment that cannot be captured with the offline method. First of all, an offline policy is trained in an emulated environment, and there are likely to be differences in real-life interactions beyond those captured in the emulated environment. When the policy is deployed in the real-life environment, these differences need to be taken into consideration in a short period of time. For example, if the learning agent is intended to perform predictive hardware maintenance and is trained in an emulated environment, real-world breakage rates under normal use conditions may not be ask expected (thus requiring the learning agent to adjust its maintenance policies). If the learning agent is tasked with presenting digital advertisements to users, a subpopulation of the users may be unexpectedly offended by the advertisements, necessitating that the agent quickly stop showing the advertisements in question.

The exemplary framework described herein addresses these and other issues in a two-stage approach, as illustrated inFIG. 13A. Note thatFIG. 13Ais intended to provide a succinct overview of the framework; each portion of the framework will be discussed in more detail with respect toFIGS. 14-16.

As illustrated inFIG. 13A, historical data1302about the environment in which the learner agent operates is initially received. The historical data1302may depend on the type of environment being emulated; for instance, if the learner agent is responsible for predictive maintenance, the historical data may include past hardware breakage rates and maintenance schedules. If the learner agent is responsible for coordinating an advertising campaign, for example, then historical information about the efficacy of previous advertising campaigns may be used. The historical data1302may be used to build a model of the environment in block1304.

At block1306, a reasonable policy is trained using the model of the environment. One suitable algorithm for training the policy is the Deep Q-Network (DQN) algorithm.

The policy, once initially trained, may be applied as a warm start policy to an online algorithm at block1308. The online algorithm operates as a second stage configured to maximize the long-term reward while learning the dynamic elements of the complex environments. An example of a suitable online algorithm is the Deep Concurrent Temporal Difference (DCTD) algorithm.

The online portion of the approach may be used in a setting like the one depicted inFIG. 13B. Here, the learner agent1352(e.g., embodied by the DCTD algorithm) interacts with multiple environments1354-1,1354-2, . . . ,1354-n. The learner agent1352is capable of taking an action in one of the environments1354-1(which, at any given time point, may be no action or the null action). The learner agent1352chooses between different available actions according to its policy at the current time. The learner agent1352then observes the consequences of the action (e.g., the amount of value accrued, which may be aggregated over time) in the environment1354-1and determines whether to adjust its policy. If the learner agent1352adjusts its policy, it can apply the adjusted policy in a different environment1354-2and repeat the process. Therefore, information learned in one environment is carried over into others, allowing the agent1352to quickly alter its policies in view of real-world experience.

As noted above, the learner agent chooses between available actions based on the current context, or environment dynamic, in an environment at the present time.FIG. 14depicts an exemplary data structure representing such an environment dynamic1400.

In a reinforcement learning setting, the agent interacts with its environment ε. Such an interaction may be formalized by a Markov Decision Process (MDP) described by a tuple (S, A, Pti, Rti). S defines the available state space1402, which includes a plurality of possible states1402-1,1402-2, . . . ,1402-n. A represents the available actions that can be taken at any given time, and defines an action space1404including a plurality of possible actions1404-1,1404-2, . . .1404-n.

Given the current state si∈S1402at time t, the agent takes an action atfrom the set of available actions A1404with respect to its policy π. The policy π is defined as a mapping from the set of states space to the actions set.

In response, the agent receives some value rt∈Rtias a reward and observes the next state st+1(denoted inFIG. 14by the available next states1406-1,1406-2, . . .1406-nin the next state space1406). Note that, in some embodiments, a given action in a particular environment might probabilistically cause the environment to transition to different states (e.g., action1404-1might transition to next state1406-1with probability p1and to next state1406-2with probability p2). These probabilities may be given by Pti.

We denote by
Rt=Σi=t∞=γi-triEq. (1)
the accumulated discounted value after time t with discount factor γ. The objective is to find an optimal policy π* which maximizes the long-term discounted value Q(s, a) from any states and following policy π*, where Q(s, a) is given by:
Q(s,a)=E(Rt|s,a)  Eq. (2)

When the state space is large, it is unavoidable to use function approximators instead of directly calculating Q(s, a) for every state-action pair. One option is to use a deep neural network Q(s, a; θ)≈Q(s, a) because of its high capability in approximating complex non-linear functions. Updates to θ can be obtained by various reinforcement learning algorithms such as Q-learning where loss function:

The DQN algorithm used in the above-described offline phase uses such a function approximator. Such a DQN algorithm relies on relatively little knowledge about the environment and primarily learns using sensory information and reward signals.

FIG. 15depicts exemplary offline training logic1500for training the initial policy using the DQN algorithm in the offline phase. Initially, a simulator is built by training a multilayer fully-connected neural network to predict the probability of the occurrence of a reward from a given state and action. Similarly, another network is trained to predict the amount of reward from a given state and action. A simulator suitable for use in exemplary embodiments is described inCustomer Simulation for Direct Marketing Experimentsby Y. Tkachenko et al.

By decoupling the output of these two networks, the reward amounts can be identified. Based on the reward amounts, policy weights are initialized at block1502. The policy weights may be initialized in a greedy manner (e.g., by assigning the most weight to the policy that achieves the highest amount of value immediately), or may be initialized in other manners (e.g., random).

Next, we consider an agent interacting with a set of users C. In exemplary embodiments, we consider the case that at every time step t, the agent is required to take an action only for a subset of users Cta⊂C and for the rest of the users the agent chooses to do no action. For example, in a predictive maintenance scenario in which server farms are maintained in different locations, the agent may choose to perform maintenance on servers in one facility but not another. Similarly, at every time t, the agent updates its policy based on a subset of the users Ctu⊂C. Ctu⊂C may include users associated with a positive value at time t, or the users with no recent activity (to model attrition). The set of users C may vary over time, as new users are added or removed.

τt, t=0, 1, . . . represents the sequence of time points when either the agent needs to take an action for a user or update the policy (i.e., τtis the time point where Ctu∪Cta≠Ø). At each time step t, the agent observes the state of each customer i∈Ctadenoted by sti(block1504) and chooses an action atifrom the set of available actions A (block1506). After executing the actions, the agent observes the value of the users (block1508) and updates their next states (block1510). At block1510, the agent may optionally update its policy weights if one course of action yields more or less value than expected. The process then repeats (block1512) for each available time step. At the end of the training process, the policy weights yielding the highest value over time are chosen to be used in the online process (block1514).

During this offline training, each user is treated individually; the agent interacts with each until the end of the time steps in block1514before moving on to the next user. Consequently, the agent learns from the entire user trajectory, which is an advantageous result that would not be achieved in a live environment.

It is noteworthy that the simulator might not represent the “real” environment, and as a result the policy trained using the simulator might differ from the optimal one. Nonetheless, in practical tests the online algorithm (depicted inFIG. 16) typically addresses these inaccuracies and adapts relatively quickly.

According to exemplary embodiments, an extension of the concurrent temporal difference (TD) algorithm may be used in the online process. During the online process, a stream of information describing present dynamics of the environment is received. Time progresses linearly, so it is not possible in the live environment to follow one user through time before updating the policies and applying them to other users.

As shown in the online training logic1600depicted inFIG. 16, the learner agent first chooses (block1602) a live environment with which to interact—as previously noted in connection withFIG. 13B, the agent interacts with multiple different environments to learn appropriate policies and apply them in new contexts. At block1604, the learner agent retrieves the stream of information representing the current environment's dynamics and determines the current state of the environment.

At block1606, the learner agent selects an action to be taken based on the current state of the environment and the policy traits (as pre-trained by the offline training process and subsequently modified by feedback loops through the online training process). According to one embodiment, the agent may use an ∈-greedy rule to select the actions; in an ∈-greedy rule, the actions are selected at random with probability ∈, and otherwise according to the agent's policy. This allows for some variation and experimentation while generally adhering to the trained policy. According to some embodiments, the value of ∈ may be fixed through the online process. Preferably, ∈ is selected so as to be not so large as to encourage excessive random actions, but not so small so as to limit the exploratory behavior of the algorithm.

The agent applies the selected action and observes the next state of the environment (as well as the accumulated value) at block1608. At block1610, the agent determines if retraining of the policy weights is needed. Different criterion may be used to determine if retraining is needed or, in some embodiments, retraining may be applied at every time step.

If retraining is to be applied, then at block1612the agent adjusts the policy weights. In some embodiments, policy weight adjustment may be performed based on user batching, as illustrated at block1614. In these embodiments, users are assigned to a batch Btj(each user may be permitted to appear in more than one batch, which is more data efficient than assigning each user to a disjoint batch) and policy weights are adjusted based on the batch by updating the policy weights in the direction of reducing the temporal difference error according to Eq (4):

It has been found that the combination of concurrent TD with deep neural networks may be fundamentally unstable. To address this issue, a non-trainable target network with weights θ−in which its weights are synchronized with the original network every K training steps.

The process may repeat (block1616) as long as actions are received for the various live environments. When there is no more data to be processed, processing may terminate (block1618).

In pseudo-code, the above described procedure could be represented as follows:

FIG. 17AandFIG. 17Bdepict experimental results of applying the above-described framework to a customer journey problem, using data from a marketing dataset (KDD1998) for an outbound mailing campaign. In this example, a DNN was used for both the DQN and DCTD algorithms, where the DNN is fully connected and uses four hidden layers and ReLU activation. The DQN network was trained for 100,000 users, each propagated for 23 periods. The DCTD algorithm is also concurrently trained with 153,000 users. A minibatch of 256 users was selected at every time-step of the DCTD algorithm and bt=600. For DCTD, γ was set to 0.99 and E was set to 0.1.

For comparison, three benchmark policies are provided: (i) an original policy, which uses the actions stored in the original training dataset; (ii) a random policy, in which the action for each user in each period is picked randomly; and (iii) a myopic policy which chooses actions with the maximum immediate reward (i.e., long-term rewards are not taken into account).

FIG. 17Ashows the results when only a DQN (offline training) algorithm is used, compared to the benchmark policies. As depicted, the policy learned by the DQN has a higher long-term reward as compared to the other policies. When an online DCTD algorithm is pre-trained with a DQN warm-start (FIG. 17B), the result follows the DQN path initially but quickly improves as the DCTD algorithm detects live system dynamics. By adapting its policy, DCTD is able to provide far better cumulative rewards compared to the offline algorithms. These experiments show a significant positive impact from utilizing the proposed framework.

In some implementations, for example, the learner agent may employ a mechanical robotic arm that is used to automatically stack bricks in an outdoor construction environment with the live system dynamics described herein, or a robotic arm that is used to automatically assemble parts on various automobiles in an automobile assembly factory environment using computer vision and multiple physical sensors with the warm start and live system dynamics described herein. In other implementations, various applications of the disclosed framework in Robotic Process Automation (RPA) can lead to large-scale improvements in repetitive maintenance environments, the robotic augmentation of activities, and the automation of tasks that are beyond the physical, perceptual and workflow abilities of humans. The preceding description provides example embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the description of the example embodiments provides those skilled in the art with an enabling description for implementing an example embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the technology as set forth in the appended claims.