Presentation 2003/1/24
Policy Gradient Method in Multi-Agent Systems : Pursuit Problem
Seiji ISHIHARA, Harukazu IGARASHI,
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Abstract(in English) We propose a method using the policy gradient for reinforcement learning in multi-agent systems. In our approach, motion planning problems in multi-agent systems are formulated as problems that each agent selects its actions to minimize each objective function independently. The objective function can be defined by a state-value function, the sum of weight parameters of state-action rules, and heuristic potentials. The functions include some parameters. The parameters are updated stochastically in order to maximize the expectation of the reward based on a history of states and actions in each episode. The results of experiments for the pursuit problem showed that our method can make short episode plans as Q-learning does, and can easily deal with limitations such as time-window restrictions imposed on the episode length and heuristic knowledge such as an attractive potential to the target.
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Keyword(in English) reinforcement learning / policy gradient method / pursuit problem / multi-agent system
Paper # AI2002-58
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Committee AI
Conference Date 2003/1/24(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Policy Gradient Method in Multi-Agent Systems : Pursuit Problem
Sub Title (in English)
Keyword(1) reinforcement learning
Keyword(2) policy gradient method
Keyword(3) pursuit problem
Keyword(4) multi-agent system
1st Author's Name Seiji ISHIHARA
1st Author's Affiliation School of Engineering,Kinki University()
2nd Author's Name Harukazu IGARASHI
2nd Author's Affiliation School of Engineering,Kinki University
Date 2003/1/24
Paper # AI2002-58
Volume (vol) vol.102
Number (no) 615
Page pp.pp.-
#Pages 6
Date of Issue