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2008

A Multiagent Reinforcement Learning Algorithm with Non-linear Dynamics

8 years 6 months ago
A Multiagent Reinforcement Learning Algorithm with Non-linear Dynamics
Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents' decisions. Due to the complexity of the problem, the majority of the previously developed MARL algorithms assumed agents either had some knowledge of the underlying game (such as Nash equilibria) and/or observed other agents actions and the rewards they received. We introduce a new MARL algorithm called the Weighted Policy Learner (WPL), which allows agents to reach a Nash Equilibrium (NE) in benchmark 2-player-2-action games with minimum knowledge. Using WPL, the only feedback an agent needs is its own local reward (the agent does not observe other agents actions or rewards). Furthermore, WPL does not assume that agents know the underlying game or the corresponding Nash Equilibrium a priori. We experimentally show that our algorithm converges in benchmark two-player-two-action games. We also show that our algorithm converges in the challenging Shapley's game where previous MAR...
Sherief Abdallah, Victor R. Lesser
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2008
Where JAIR
Authors Sherief Abdallah, Victor R. Lesser
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