Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach

Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach

This paper focuses on a multi-agent cooperation which is generally difficult to be achieved without sufficient information of other agents, and proposes the reinforcement learning method that introduces an internal reward for a multi-agent cooperation without sufficient information. To guarantee to...

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Bibliographic Details
Main Authors: Fumito Uwano, Naoki Tatebe, Masaya Nakata, Keiki Takadama, Tim Kovacs
Format: Article
Language:English
Published: European Alliance for Innovation (EAI) 2016-12-01
Series:EAI Endorsed Transactions on Collaborative Computing
Subjects:
multi-agent system
analysis
q-learning
internal reward
Online Access:http://eudl.eu/doi/10.4108/eai.3-12-2015.2262878

Internet

http://eudl.eu/doi/10.4108/eai.3-12-2015.2262878

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