Multiagent Reinforcement Learning with Regret Matching for Robot Soccer

作者：Liu, Qiang^*; Ma, Jiachen; Xie, Wei

来源：Mathematical Problems in Engineering, 2013, 2013: 926267.

DOI：10.1155/2013/926267

• MOBILE ROBOTS
• COOPERATION

This paper proposes a novel multiagent reinforcement learning (MARL) algorithm Nash-Q learning with regret matching, in which regret matching is used to speed up the well-known MARL algorithm Nash-Q 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-Q 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-Q learning algorithm can be increased. Simulation results on robot soccer validate that compared to original Nash-Q learning algorithm, the use of regret matching during the learning phase of Nash-Q learning has excellent ability of online learning and results in significant performance in terms of scores, average reward and policy convergence.

• 出版日期2013
• 单位哈尔滨工业大学