Above are papers related to the following paper: [Paper_ID] Paper #170338. 	[Title] Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study [Abstract] We use simulated soccer to study multiagent learning. Each team's players (agents) share action set and policy but may behave differently due to position-dependent inputs. All agents making up a team are rewarded or punished collectively in case of goals. We conduct simulations with varying team sizes, and compare two learning algorithms: TD-Q learning with linear neural networks (TD-Q) and Probabilistic Incremental Program Evolution (PIPE). TD-Q is based on evaluation functions (EFs) mapping input/action pairs to expected reward, while PIPE searches policy space directly. PIPE uses adaptive "probabilistic prototype trees" to synthesize programs that calculate action probabilities from current inputs. Our results show that TD-Q encounters several difficulties in learning appropriate shared EFs. PIPE, however, does not depend on EFs and can find good policies faster and more reliably. This suggests that in multiagent learning scenarios direct search through policy space can offer advantages over EF-based approaches. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114629. 	[Title] Spurious Solutions to the Bellman Equation [Abstract] Reinforcement learning algorithms often work by finding functions that satisfy the Bellman equation. This yields an optimal solution for prediction with Markov chains and for controlling a Markov decision process (MDP) with a finite number of states and actions. This approach is also frequently applied to Markov chains and MDPs with infinite states. We show that, in this case, the Bellman equation may have multiple solutions, many of which lead to erroneous predictions and policies (Baird, 1996). Algorithms and conditions are presented that guarantee a single, optimal solution to the Bellman equation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105033. 	[Title] Learning to take risks [Abstract] Agents that learn about other agents and can exploit this information possess a distinct advantage in competitive situations. Games provide stylized adversarial environments to study agent learning strategies. Researchers have developed game playing programs that learn to play better from experience. We have developed a learning program that does not learn to play better, but learns to identify and exploit the weaknesses of a particular opponent by repeatedly playing it over several games. We propose a scheme for learning opponent action probabilities and a utility maximization framework that exploits this learned opponent model. We show that the proposed expected utility maximization strategy generalizes the traditional maximin strategy, and allows players to benefit by taking calculated risks that are avoided by the max-imin strategy. Experiments in the popular board game of Connect-4 show that a learning player consistently outperforms a non-learning player when pitted against another automated player using a weaker heuristic. Though our proposed mechanism does not improve the skill level of a computer player, it does improve its ability to play more effectively against a weaker opponent. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13208. 	[Title] ON LEARNING HOW TO LEARN LEARNING STRATEGIES [Abstract] This paper introduces the "incremental self-improvement paradigm". Unlike previous methods, incremental self-improvement encourages a reinforcement learning system to improve the way it learns, and to improve the way it improves the way it learns ..., without significant theoretical limitations | the system is able to "shift its inductive bias" in a universal way. Its major features are: (1) There is no explicit difference between "learning", "meta-learning", and other kinds of information processing. Using a Turing machine equivalent programming language, the system itself occasionally executes self-delimiting, initially highly random "self-modification programs" which modify the context-dependent probabilities of future action sequences (including future self-modification programs). (2) The system keeps only those probability modifications computed by "useful" self-modification programs: those which bring about more payoff (reward, reinforcement) per time than all previous self-modification programs. (3) The computation of payoff per time takes into account all the computation time required for learning | the entire system life is considered: boundaries between learning trials are ignored (if there are any). A particular implementation based on the novel paradigm is presented. It is designed to exploit what conventional digital machines are good at: fast storage addressing, arithmetic operations etc. Experiments illustrate the system's mode of operation. Keywords: Self-improvement, self-reference, introspection, machine-learning, reinforcement learning. Note: This is the revised and extended version of an earlier report from November 24, 1994. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6169. 	[Title] In  Improving Elevator Performance Using Reinforcement Learning [Abstract] This paper describes the application of reinforcement learning (RL) to the difficult real world problem of elevator dispatching. The elevator domain poses a combination of challenges not seen in most RL research to date. Elevator systems operate in continuous state spaces and in continuous time as discrete event dynamic systems. Their states are not fully observable and they are nonstationary due to changing passenger arrival rates. In addition, we use a team of RL agents, each of which is responsible for controlling one elevator car. The team receives a global reinforcement signal which appears noisy to each agent due to the effects of the actions of the other agents, the random nature of the arrivals and the incomplete observation of the state. In spite of these complications, we show results that in simulation surpass the best of the heuristic elevator control algorithms of which we are aware. These results demonstrate the power of RL on a very large scale stochastic dynamic optimization problem of practical utility. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13212. 	[Title] DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997 [Abstract] Many neural net learning algorithms aim at finding "simple" nets to explain training data. The expectation is: the "simpler" the networks, the better the generalization on test data (! Occam's razor). Previous implementations, however, use measures for "simplicity" that lack the power, universality and elegance of those based on Kolmogorov complexity and Solomonoff's algorithmic probability. Likewise, most previous approaches (especially those of the "Bayesian" kind) suffer from the problem of choosing appropriate priors. This paper addresses both issues. It first reviews some basic concepts of algorithmic complexity theory relevant to machine learning, and how the Solomonoff-Levin distribution (or universal prior) deals with the prior problem. The universal prior leads to a probabilistic method for finding "algorithmically simple" problem solutions with high generalization capability. The method is based on Levin complexity (a time-bounded generalization of Kolmogorov complexity) and inspired by Levin's optimal universal search algorithm. For a given problem, solution candidates are computed by efficient "self-sizing" programs that influence their own runtime and storage size. The probabilistic search algorithm finds the "good" programs (the ones quickly computing algorithmically probable solutions fitting the training data). Simulations focus on the task of discovering "algorithmically simple" neural networks with low Kolmogorov complexity and high generalization capability. It is demonstrated that the method, at least with certain toy problems where it is computationally feasible, can lead to generalization results unmatchable by previous neural net algorithms. Much remains do be done, however, to make large scale applications and "incremental learning" feasible. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28456. 	[Title] A Competitive Approach to Game Learning [Abstract] Machine learning of game strategies has often depended on competitive methods that continually develop new strategies capable of defeating previous ones. We use a very inclusive definition of game and consider a framework within which a competitive algorithm makes repeated use of a strategy learning component that can learn strategies which defeat a given set of opponents. We describe game learning in terms of sets H and X of first and second player strategies, and connect the model with more familiar models of concept learning. We show the importance of the ideas of teaching set [20] and specification number [19] k in this new context. The performance of several competitive algorithms is investigated, using both worst-case and randomized strategy learning algorithms. Our central result (Theorem 4) is a competitive algorithm that solves games in a total number of strategies polynomial in lg(jHj), lg(jX j), and k. Its use is demonstrated, including an application in concept learning with a new kind of counterexample oracle. We conclude with a complexity analysis of game learning, and list a number of new questions arising from this work. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #682666. 	[Title] Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm [Abstract] In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games to a broader framework. We design a multiagent Q-learning method under this framework, and prove that it converges to a Nash equilibrium under specified conditions. This algorithm is useful for finding the optimal strategy when there exists a unique Nash equilibrium in the game. When there exist multiple Nash equilibria in the game, this algorithm should be combined with other learning techniques to find optimal strategies. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20526. 	[Title] Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning [Abstract] Many factory optimization problems, from inventory control to scheduling and reliability, can be formulated as continuous-time Markov decision processes. A primary goal in such problems is to find a gain-optimal policy that minimizes the long-run average cost. This paper describes a new average-reward algorithm called SMART for finding gain-optimal policies in continuous time semi-Markov decision processes. The paper presents a detailed experimental study of SMART on a large unreliable production inventory problem. SMART outperforms two well-known reliability heuristics from industrial engineering. A key feature of this study is the integration of the reinforcement learning algorithm directly into two commercial discrete-event simulation packages, ARENA and CSIM, paving the way for this approach to be applied to many other factory optimization problems for which there already exist simulation models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1103162. 	[Title] Team-Partitioned, Opaque-Transition Reinforcement Learning [Abstract] In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of using action-dependent features to generalize the state space. In our work, we use a learned action-dependent feature space. TPOT-RL is an effective technique to allow a team of agents to learn to cooperate towards the achievement of a specific goal. It is an adaptation of traditional RL methods that is applicable in complex, non-Markovian, multi-agent domains with large state spaces and limited training opportunities. Multi-agent scenarios are opaque-transition, as team members are not always in full communication with one another and adversaries may affect the environment. Hence, each learner cannot rely on having knowledge of future state transitions after acting in the world. TPOT-RL enables teams of agents to learn effective policies with very few training examples even in the face of a large state space with large amounts of hidden state. The main responsible features are: dividing the learning task among team members, using a very coarse, action-dependent feature space, and allowing agents to gather reinforcement directly from observation of the environment. TPOT-RL is fully implemented and has been tested in the robotic soccer domain, a complex, multi-agent framework. This paper presents the algorithmic details of TPOT-RL as well as empirical results demonstrating the effectiveness of the developed multi-agent learning approach with learned features. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28227. 	[Title] Reinforcement Learning Methods for Continuous-Time Markov Decision Problems [Abstract] Semi-Markov Decision Problems are continuous time generalizations of discrete time Markov Decision Problems. A number of reinforcement learning algorithms have been developed recently for the solution of Markov Decision Problems, based on the ideas of asynchronous dynamic programming and stochastic approximation. Among these are TD(), Q-learning, and Real-time Dynamic Programming. After reviewing semi-Markov Decision Problems and Bellman's optimality equation in that context, we propose algorithms similar to those named above, adapted to the solution of semi-Markov Decision Problems. We demonstrate these algorithms by applying them to the problem of determining the optimal control for a simple queueing system. We conclude with a discussion of circumstances under which these algorithms may be usefully ap plied. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17476. 	[Title] Markov games as a framework for multi-agent reinforcement learning [Abstract] In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsistic view, secondary agents can only be part of the environment and are therefore fixed in their behavior. The framework of Markov games allows us to widen this view to include multiple adaptive agents with interacting or competing goals. This paper considers a step in this direction in which exactly two agents with diametrically opposed goals share an environment. It describes a Q-learning-like algorithm for finding optimal policies and demonstrates its application to a simple two-player game in which the optimal policy is probabilistic. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1130567. 	[Title] NEUROCONTROL BY REINFORCEMENT LEARNING [Abstract] Reinforcement learning (RL) is a model-free tuning and adaptation method for control of dynamic systems. Contrary to supervised learning, based usually on gradient descent techniques, RL does not require any model or sensitivity function of the process. Hence, RL can be applied to systems that are poorly understood, uncertain, nonlinear or for other reasons untractable with conventional methods. In reinforcement learning, the overall controller performance is evaluated by a scalar measure, called reinforcement. Depending on the type of the control task, reinforcement may represent an evaluation of the most recent control action or, more often, of an entire sequence of past control moves. In the latter case, the RL system learns how to predict the outcome of each individual control action. This prediction is then used to adjust the parameters of the controller. The mathematical background of RL is closely related to optimal control and dynamic programming. This paper gives a comprehensive overview of the RL methods and presents an application to the attitude control of a satellite. Some well known applications from the literature are reviewed as well. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #214472. 	[Title] A Computer Scientist's View of Life, the Universe, and Everything [Abstract] Is the universe computable? If so, it may be much cheaper in terms of information requirements to compute all computable universes instead of just ours. I apply basic concepts of Kolmogorov complexity theory to the set of possible universes, and chat about perceived and true randomness, life, generalization, and learning in a given universe. Assumptions. A long time ago, the Great Programmer wrote a program that runs all possible universes on His Big Computer. "Possible" means "computable": (1) Each universe evolves on a discrete time scale. (2) Any universe's state at a given time is describable by a finite number of bits. One of the many universes is ours, despite some who evolved in it and claim it is incomputable. Computable universes. Let T M denote an arbitrary universal Turing machine with unidirectional output tape. T M 's input and output symbols are "0", "1", and "," (comma). T M 's possible input programs can be ordered alphabetically: "" (empty program), "0", "1", ",", "00", "01", "0,", "10", "11", "1,", ",0", ",1", ",,", "000", etc. Let A k denote T M 's k-th program in this list. Its output will be a finite or infinite string over the alphabet f "0","1",","g. This sequence of bitstrings separated by commas will be interpreted as the evolution E k of universe U k . If E k includes at least one comma, then let U l k represents U k 's state at the l-th time step of E k (k; l 2 f1; 2; : : : ; g). E k is represented by the sequence U 1 k corresponds to U k 's big bang. Different algorithms may compute the same universe. Some universes are finite (those whose programs cease producing outputs at some point), others are not. I don't know about ours. TM not important. The choice of the Turing machine is not important. This is due to the compiler theorem: for each universal Turing machine C there exists a constant prefix C 2 f "0","1",","g fl such that for all possible programs p, C's output in response to program C p is identical to T M 's output in response to p. The prefix C is the compiler that compiles programs for T M into equivalent programs for C. k denote the l-th (possibly empty) bitstring before the l-th comma. U l Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13269. 	[Title] database [Abstract] MIT Computational Cognitive Science Technical Report 9701 Abstract We describe variational approximation methods for efficient probabilistic reasoning, applying these methods to the problem of diagnostic inference in the QMR-DT database. The QMR-DT database is a large-scale belief network based on statistical and expert knowledge in internal medicine. The size and complexity of this network render exact probabilistic diagnosis infeasible for all but a small set of cases. This has hindered the development of the QMR- DT network as a practical diagnostic tool and has hindered researchers from exploring and critiquing the diagnostic behavior of QMR. In this paper we describe how variational approximation methods can be applied to the QMR network, resulting in fast diagnostic inference. We evaluate the accuracy of our methods on a set of standard diagnostic cases and compare to stochastic sampling methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #358884. 	[Title] THE DISCOVERY OF ALGORITHMIC PROBABILITY [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106406. 	[Title] Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in [Abstract] In cellular telephone systems, an important problem is to dynamically allocate the communication resource (channels) so as to maximize service in a stochastic caller environment. This problem is naturally formulated as a dynamic programming problem and we use a reinforcement learning (RL) method to find dynamic channel allocation policies that are better than previous heuristic solutions. The policies obtained perform well for a broad variety of call traffic patterns. We present results on a large cellular system In cellular communication systems, an important problem is to allocate the communication resource (bandwidth) so as to maximize the service provided to a set of mobile callers whose demand for service changes stochastically. A given geographical area is divided into mutually disjoint cells, and each cell serves the calls that are within its boundaries (see Figure 1a). The total system bandwidth is divided into channels, with each channel centered around a frequency. Each channel can be used simultaneously at different cells, provided these cells are sufficiently separated spatially, so that there is no interference between them. The minimum separation distance between simultaneous reuse of the same channel is called the channel reuse constraint . When a call requests service in a given cell either a free channel (one that does not violate the channel reuse constraint) may be assigned to the call, or else the call is blocked from the system; this will happen if no free channel can be found. Also, when a mobile caller crosses from one cell to another, the call is "handed off" to the cell of entry; that is, a new free channel is provided to the call at the new cell. If no such channel is available, the call must be dropped/disconnected from the system. One objective of a channel allocation policy is to allocate the available channels to calls so that the number of blocked calls is minimized. An additional objective is to minimize the number of calls that are dropped when they are handed off to a busy cell. These two objectives must be weighted appropriately to reflect their relative importance, since dropping existing calls is generally more undesirable than blocking new calls. with approximately 70 49 states. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #892139. 	[Title] Using Communication to Reduce Locality in Distributed Multi-Agent Learning [Abstract] This paper attempts to bridge the fields of machine learning, robotics, and distributed AI. It discusses the use of communication in reducing the undesirable effects of locality in fully distributed multi-agent systems with multiple agents/robots learning in parallel while interacting with each other. Two key problems, hidden state and credit assignment, are addressed by applying local undirected broadcast communication in a dual role: as sensing and as reinforcement. The methodology is demonstrated on two multi-robot learning experiments. The first describes learning a tightly-coupled coordination task with two robots, the second a loosely-coupled task with four robots learning social rules. Communication is used to 1) share sensory data to overcome hidden state and 2) share reinforcement to overcome the credit assignment problem between the agents and bridge the gap between local/individual and global/group payoff. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6385. 	[Title] Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents [Abstract] Intelligent human agents exist in a cooperative social environment that facilitates learning. They learn not only by trial-and-error, but also through cooperation by sharing instantaneous information, episodic experience, and learned knowledge. The key investigations of this paper are, "Given the same number of reinforcement learning agents, will cooperative agents outperform independent agents who do not communicate during learning?" and "What is the price for such cooperation?" Using independent agents as a benchmark, cooperative agents are studied in following ways: (1) sharing sensation, (2) sharing episodes, and (3) sharing learned policies. This paper shows that (a) additional sensation from another agent is beneficial if it can be used efficiently, (b) sharing learned policies or episodes among agents speeds up learning at the cost of communication, and (c) for joint tasks, agents engaging in partnership can significantly outperform independent agents although they may learn slowly in the beginning. These tradeoffs are not just limited to multi-agent reinforcement learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #117. 	[Title] Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms [Abstract] The problem of maximizing the expected total discounted reward in a completely observable Markovian environment, i.e., a Markov decision process (mdp), models a particular class of sequential decision problems. Algorithms have been developed for making optimal decisions in mdps given either an mdp specification or the opportunity to interact with the mdp over time. Recently, other sequential decision-making problems have been studied prompting the development of new algorithms and analyses. We describe a new generalized model that subsumes mdps as well as many of the recent variations. We prove some basic results concerning this model and develop generalizations of value iteration, policy iteration, model-based reinforcement-learning, and Q-learning that can be used to make optimal decisions in the generalized model under various assumptions. Applications of the theory to particular models are described, including risk-averse mdps, exploration-sensitive mdps, sarsa, Q-learning with spreading, two-player games, and approximate max picking via sampling. Central to the results are the contraction property of the value operator and a stochastic-approximation theorem that reduces asynchronous convergence to synchronous convergence. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #33013. 	[Title] Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates [Abstract] Fully cooperative multiagent systemsthose in which agents share a joint utility modelis of special interest in AI. A key problem is that of ensuring that the actions of individual agents are coordinated, especially in settings where the agents are autonomous decision makers. We investigate approaches to learning coordinated strategies in stochastic domains where an agent's actions are not directly observable by others. Much recent work in game theory has adopted a Bayesian learning perspective to the more general problem of equilibrium selection, but tends to assume that actions can be observed. We discuss the special problems that arise when actions are not observable, including effects on rates of convergence, and the effect of action failure probabilities and asymmetries. We also use likelihood estimates as a means of generalizing fictitious play learning models in our setting. Finally, we propose the use of maximum likelihood as a means of removing strategies from consideration, with the aim of convergence to a conventional equilibrium, at which point learning and deliberation can cease. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #887. 	[Title] Some studies in machine learning using the game of checkers. IBM Journal, 3(3):211-229, 1959. Some [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129208. 	[Title] Figure 3: Average model size accepted from a ran-dom prefix-closed samples of various size, and [Abstract] that is based on Angluin's L fl algorithm. The algorithm maintains a model consistent with its past examples. When a new counterexample arrives it tries to extend the model in a minimal fashion. We conducted a set of experiments where random automata that represent different strategies were generated, and the algorithm tried to learn them based on prefix-closed samples of their behavior. The algorithm managed to learn very compact models that agree with the samples. The size of the sample had a small effect on the size of the model. The experimental results suggest that for random prefix-closed samples the algorithm behaves well. However, following Angluin's result on the difficulty of learning almost uniform complete samples [ An-gluin, 1978 ] , it is obvious that our algorithm does not solve the complexity issue of inferring a DFA from a general prefix-closed sample. We are currently looking for classes of prefix-closed samples in which US-L* behaves well. [ Carmel and Markovitch, 1994 ] D. Carmel and S. Markovitch. The M* algorithm: Incorporating opponent models into adversary search. Technical Report CIS report 9402, Technion, March 1994. [ Carmel and Markovitch, 1995 ] D. Carmel and S. Markovitch. Unsupervised learning of finite automata: A practical approach. Technical Report CIS report 9504, Technion, March 1995. [ Shoham and Tennenholtz, 1994 ] Y. Shoham and M. Tennenholtz. Co-Learning and the evolution of social activity. Technical Report STAN-CS-TR-94-1511, Stanford Univrsity, Department of Computer Science, 1994. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1122425. 	[Title] Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems [Abstract] The application of decision making and learning algorithms to multi-agent systems presents many interestingresearch challenges and opportunities. Among these is the ability for agents to learn how to act by observing or imitating other agents. We describe an algorithm, the IQ-algorithm, that integrates imitation with Q-learning. Roughly, a Q-learner uses the observations it has made of an expert agent to bias its exploration in promising directions. This algorithm goes beyond previous work in this direction by relaxing the oft-made assumptions that the learner (observer) and the expert (observed agent) share the same objectives and abilities. Our preliminary experiments demonstrate significant transfer between agents using the IQ-model and in many cases reductions in training time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #131318. 	[Title] FLAT MINIMA Neural Computation 9(1):1-42 (1997) [Abstract] We present a new algorithm for finding low complexity neural networks with high generalization capability. The algorithm searches for a "flat" minimum of the error function. A flat minimum is a large connected region in weight-space where the error remains approximately constant. An MDL-based, Bayesian argument suggests that flat minima correspond to "simple" networks and low expected overfitting. The argument is based on a Gibbs algorithm variant and a novel way of splitting generalization error into underfitting and overfitting error. Unlike many previous approaches, ours does not require Gaussian assumptions and does not depend on a "good" weight prior instead we have a prior over input/output functions, thus taking into account net architecture and training set. Although our algorithm requires the computation of second order derivatives, it has backprop's order of complexity. Automatically, it effectively prunes units, weights, and input lines. Various experiments with feedforward and recurrent nets are described. In an application to stock market prediction, flat minimum search outperforms (1) conventional backprop, (2) weight decay, (3) "optimal brain surgeon" / "optimal brain damage". We also provide pseudo code of the algorithm (omitted from the NC-version). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #7419. 	[Title] A Neuro-Dynamic Programming Approach to Retailer Inventory Management 1 [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105148. 	[Title] ENVIRONMENT-INDEPENDENT REINFORCEMENT ACCELERATION  difference between time and space is that you can't reuse time. [Abstract] A reinforcement learning system with limited computational resources interacts with an unrestricted, unknown environment. Its goal is to maximize cumulative reward, to be obtained throughout its limited, unknown lifetime. System policy is an arbitrary modifiable algorithm mapping environmental inputs and internal states to outputs and new internal states. The problem is: in realistic, unknown environments, each policy modification process (PMP) occurring during system life may have unpredictable influence on environmental states, rewards and PMPs at any later time. Existing reinforcement learning algorithms cannot properly deal with this. Neither can naive exhaustive search among all policy candidates | not even in case of very small search spaces. In fact, a reasonable way of measuring performance improvements in such general (but typical) situations is missing. I define such a measure based on the novel "reinforcement acceleration criterion" (RAC). At a given time, RAC is satisfied if the beginning of each completed PMP that computed a currently valid policy modification has been followed by long-term acceleration of average reinforcement intake (the computation time for later PMPs is taken into account). I present a method called "environment-independent reinforcement acceleration" (EIRA) which is guaranteed to achieve RAC. EIRA does neither care whether the system's policy allows for changing itself, nor whether there are multiple, interacting learning systems. Consequences are: (1) a sound theoretical framework for "meta-learning" (because the success of a PMP recursively depends on the success of all later PMPs, for which it is setting the stage). (2) A sound theoretical framework for multi-agent learning. The principles have been implemented (1) in a single system using an assembler-like programming language to modify its own policy, and (2) a system consisting of multiple agents, where each agent is in fact just a connection in a fully recurrent reinforcement learning neural net. A by-product of this research is a general reinforcement learning algorithm for such nets. Preliminary experiments illustrate the theory. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #411005. 	[Title] GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS [Abstract] Numerous recent papers focus on standard recurrent nets' problems with long time lags between relevant signals. Some propose rather sophisticated, alternative methods. We show: many problems used to test previous methods can be solved more quickly by random weight guessing. Please summarise all the given information.

Paper #170338 has the title `Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study  
` and the abstract `We use simulated soccer to study multiagent learning. Each team's players (agents) share action set and policy but may behave differently due to position-dependent inputs. All agents making up a team are rewarded or punished collectively in case of goals. We conduct simulations with varying team sizes, and compare two learning algorithms: TD-Q learning with linear neural networks (TD-Q) and Probabilistic Incremental Program Evolution (PIPE). TD-Q is based on evaluation functions (EFs) mapping input/action pairs to expected reward, while PIPE searches policy space directly. PIPE uses adaptive "probabilistic prototype trees" to synthesize programs that calculate action probabilities from current inputs. Our results show that TD-Q encounters several difficulties in learning appropriate shared EFs. PIPE, however, does not depend on EFs and can find good policies faster and more reliably. This suggests that in multiagent learning scenarios direct search through policy space can offer advantages over EF-based approaches. 
`.
Question: Which category should Paper #170338 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #170338 has the title `Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study  
` and the abstract `We use simulated soccer to study multiagent learning. Each team's players (agents) share action set and policy but may behave differently due to position-dependent inputs. All agents making up a team are rewarded or punished collectively in case of goals. We conduct simulations with varying team sizes, and compare two learning algorithms: TD-Q learning with linear neural networks (TD-Q) and Probabilistic Incremental Program Evolution (PIPE). TD-Q is based on evaluation functions (EFs) mapping input/action pairs to expected reward, while PIPE searches policy space directly. PIPE uses adaptive "probabilistic prototype trees" to synthesize programs that calculate action probabilities from current inputs. Our results show that TD-Q encounters several difficulties in learning appropriate shared EFs. PIPE, however, does not depend on EFs and can find good policies faster and more reliably. This suggests that in multiagent learning scenarios direct search through policy space can offer advantages over EF-based approaches. 
`.
Question: Which category should Paper #170338 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Reinforcement_Learning