user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving Search Through A3C Reinforcement Learning Based Conversational Agent

Paper content:
We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent.
Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities.
Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming.
We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent.
We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user.
We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes.
Our experiments show that the agent learns to achieve higher rewards and better states.
Within the domain of search, the recent advances have focused on personalizing the search results through recommendations BID27 BID19 .
While the quality of recommendations have improved, the conventional search interface has not innovated much to incorporate useful contextual cues which are often missed.
Conventional search interface enables the end user to perform a keyword based faceted search where the typical work flow goes as follows: the end user types in her search query, applies some filters and then modifies the query based on the results.
This iterative interaction naturally paves way for incorporating conversations in the process.
Instead of the search engine just retrieving the best result set, it can interact with the user to collect more contextual cues.
For example, if a user searches for birthday gift, the search engine could follow-up by asking who are you buying the gift for.
Such information and interaction can provide more humanlike and engaging search experience along with assisting user in discovering their search intent.
In this work we address this problem by developing a Reinforcement Learning (RL) BID21 based conversational search agent which interacts with the users to help them in narrowing down to relevant search results by providing them contextual assistance.RL based dialogue agents have been designed for tasks like restaurant, bus and hotel reservation BID18 which have limited and well-defined objective search modalities without much scope for subjective discussion.
For instance, when searching for a restaurant, the user can specify her preferences (budget, distance, cuisines etc) due to which the problem can be modeled as a slot filling exercise.
In contrast, suppose a designer is searching for digital assets (over a repository of images, videos etc) to be used in a movie poster.
She would start with a broad idea and her idea would get refined as the search progresses.
The modified search intent involves an implicit cognitive feedback which can be used to improve the search results.
We model our agent for this type of search task.
Since the user preferences can not be modeled using a fixed set of facets, we end up with a very large search space which is not the case with most other goal oriented RL agents.We model the search process as a sequence of alternate interactions between the user and the RL agent.
The extent to which the RL agent could help the user depends on the sequence and the type of actions it takes according to user behavior.
Under the RL framework, intermediate rewards is given to the agent at each step based on its actions and state of conversational search.
It learns Since true conversational data is not easily available in search domain, we propose to use query and session log data to develop a stochastic virtual user environment to simulate training episodes and bootstrap the learning of the agent.
Our agent interacts with the user to gauge user intent and treats the search engine as a black box service which makes it easily deployable over any search engine.
We perform qualitative experiments by simulating validation episodes with different reinforcement learning algorithms under various formulations of the state space to evaluate the performance of the trained agent.Our contributions are three-fold:
1) formulating conversational interactive search as a reinforcement learning problem and proposing a generic and easily extendable set of states, actions and rewards;
2) developing a stochastic user model which can be used to efficiently sample user actions while simulating an episode;
3) we develop A3C (Asynchronous Advantage Actor-Critic) BID15 algorithm based architecture to predict the policy and state value functions of RL agent and compare it with other RL algorithms over performance on validation episodes.
In this paper, we develop a Reinforcement Learning based search assistant to interact with customers to help them search digital assets suited to their use-case.
We model the rewards, state space, action space and develop an A3C based architecture which leverages the context of search to predict the policy.
The trained agent is able to obtain higher average rewards in the validation episodes with virtual user and observes states with better values indicative of providing better search experience.
We also propose a virtual stochastic user model to interact and train the RL agent in absence of labeled conversational data which accelerates the process of obtaining a bootstrapped agent.As the next step, we would deploy our system to collect true conversational data which can be used to fine tune the current model as well as to train a new model which can generate the natural language responses in addition to deciding the action.
In different search domains, designing the state and action space can take significant time which makes every situation an absolutely new task to be solved.
To approach this issue as a future work, another system can be designed which helps in the automation of state space characterization with the help of system query logs.

assistant: text: A Reinforcement Learning based conversational search assistant which provides contextual assistance in subjective search (like digital assets).