SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes

8 Mar 2018 Chengwei Zhang Xiaohong Li Jianye Hao Siqi Chen Karl Tuyls Wanli Xue

In multiagent environments, the capability of learning is important for an agent to behave appropriately in face of unknown opponents and dynamic environment. From the system designer's perspective, it is desirable if the agents can learn to coordinate towards socially optimal outcomes, while also avoiding being exploited by selfish opponents... (read more)

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