Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/78075
Title: Coordination guided reinforcement learning
Authors: Lau, Q.P.
Lee, M.L. 
Hsu, W. 
Keywords: Coordination constraints
Factored Markov decision process
Guiding exploration
Reinforcement learning
Issue Date: 2012
Source: Lau, Q.P.,Lee, M.L.,Hsu, W. (2012). Coordination guided reinforcement learning. 11th International Conference on Autonomous Agents and Multiagent Systems 2012, AAMAS 2012: Innovative Applications Track 1 : 41-48. ScholarBank@NUS Repository.
Abstract: In this paper, we propose to guide reinforcement learning (RL) with expert coordination knowledge for multi-agent problems managed by a central controller. The aim is to learn to use expert coordination knowledge to restrict the joint action space and to direct exploration towards more promising states, thereby improving the overall learning rate. We model such coordination knowledge as constraints and propose a two-level RL system that utilizes these constraints for online applications. Our declarative approach towards specifying coordination in multi-agent learning allows knowledge sharing between constraints and features (basis functions) for function approximation. Results on a soccer game and a tactical real-time strategy game show that coordination constraints improve the learning rate compared to using only unary constraints. The two-level RL system also outperforms existing single-level approach that utilizes joint action selection via coordination graphs. Copyright © 2012, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
Source Title: 11th International Conference on Autonomous Agents and Multiagent Systems 2012, AAMAS 2012: Innovative Applications Track
URI: http://scholarbank.nus.edu.sg/handle/10635/78075
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