Kagan Tumer's Publications

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Reinforcement Learning in Large Multi-agent Systems. A. Agogino and K. Tumer. In AAMAS-05 Workshop on Coordination of Large Scale Multiagent Systems, Utrecht, Netherlands, July 2005.

Abstract

Enabling reinforcement learning to be effective in large-scale multi-agent Markov Decisions Problems is a challenging task. To address this problem we propose a multi-agent variant of Q-learning: ``Q Updates with Immediate Counterfactual Rewards-learning'' (QUICR-learning). Given a global reward function over all agents that the large-scale system is trying to maximize, QUICR-learning breaks down the global reward into many agent-specific rewards that have the following two properties: 1) agents maximizing their agent-specific rewards tend to maximize the global reward, 2) an agent's action has a large influence on its agent-specific reward, allowing it to learn quickly. Each agent then uses standard Q-learning type updates to form a policy to maximize the agent-specific rewards. Results on multi-agent grid-world problems over two topologies, show that QUICR-learning can be effective with hundreds of agents and can achieve up to 300\% improvements in performance over both conventional and local Q-learning in the largest tested systems.

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BibTeX Entry

@incollection{tumer-agogino_lsmas05,
        author = {A. Agogino and K. Tumer},
        title = {Reinforcement Learning in Large Multi-agent Systems},
        booktitle = {AAMAS-05 Workshop on Coordination of Large Scale Multiagent Systems},
        address = {Utrecht, Netherlands},
	editors = {R. Mailler and P. Scerri and R. Vincent},
        month = {July},
	abstract = {Enabling reinforcement learning to be effective in large-scale multi-agent Markov Decisions Problems is a challenging task. To address this problem we propose a multi-agent variant of Q-learning: ``Q Updates with Immediate Counterfactual Rewards-learning'' (QUICR-learning). Given a global reward function over all agents that the large-scale system is trying to maximize, QUICR-learning breaks down the global reward into many agent-specific rewards that have the following two properties: 1) agents maximizing their agent-specific rewards tend to maximize the global reward, 2) an agent's action has a large influence on its agent-specific reward, allowing it to learn quickly. Each agent then uses standard Q-learning type updates to form a policy to maximize the agent-specific rewards. Results on multi-agent grid-world problems over two topologies, show that QUICR-learning can be effective with hundreds of agents and can achieve up to 300\% improvements in performance over both conventional and local Q-learning in the largest tested systems.},
	bib2html_pubtype = {Workshop/Symposium Papers},
	bib2html_rescat = {Multiagent Systems},
        year = {2005}
}

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