Multiagent Reinforcement Learning with Regret Matching for Robot Soccer
Qiang Liu,
Jiachen Ma and
Wei Xie
Mathematical Problems in Engineering, 2013, vol. 2013, 1-8
Abstract:
This paper proposes a novel multiagent reinforcement learning (MARL) algorithm Nash- learning with regret matching, in which regret matching is used to speed up the well-known MARL algorithm Nash- learning. It is critical that choosing a suitable strategy for action selection to harmonize the relation between exploration and exploitation to enhance the ability of online learning for Nash- learning. In Markov Game the joint action of agents adopting regret matching algorithm can converge to a group of points of no-regret that can be viewed as coarse correlated equilibrium which includes Nash equilibrium in essence. It is can be inferred that regret matching can guide exploration of the state-action space so that the rate of convergence of Nash- learning algorithm can be increased. Simulation results on robot soccer validate that compared to original Nash- learning algorithm, the use of regret matching during the learning phase of Nash- learning has excellent ability of online learning and results in significant performance in terms of scores, average reward and policy convergence.
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:926267
DOI: 10.1155/2013/926267
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