Multi-agent Cooperation Algorithm Based on Individual Gap Emotion in Sparse Reward Scenarios
WANG Hao1,2, WANG Jing1,2, FANG Baofu1,2
1. School of Computer Science and Information Engineering,Hefei University of Technology, Hefei 230601; 2. Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei 230601
Abstract：To address the sparse reward problem confronted by reinforcement learning in multi-agent environment, a multi-agent cooperation algorithm based on individual gap emotion is proposed grounded on the role of emotions in human learning and decision making. The approximate joint action value function is optimized end-to-end to train individual policy, and the individual action value function of each agent is taken as an evaluation of the event. A gap emotion is generated via the gap between the predicted evaluation and the actual situation. The gap emotion model is regarded as an intrinsic motivation mechanism to generate an intrinsic emotion reward for each agent as an effective supplement to the extrinsic reward. Thus, the problem of sparse extrinsic rewards is alleviated. Moreover, the intrinsic emotional reward is task-independent and consequently it possesses some generality. The effectiveness and robustness of the proposed algorithm are verified in a multi-agent pursuit scenario with different sparsity levels.
王浩, 汪京, 方宝富. 稀疏奖励场景下基于个体落差情绪的多智能体协作算法[J]. 模式识别与人工智能, 2022, 35(5): 451-460.
WANG Hao, WANG Jing, FANG Baofu. Multi-agent Cooperation Algorithm Based on Individual Gap Emotion in Sparse Reward Scenarios. Pattern Recognition and Artificial Intelligence, 2022, 35(5): 451-460.
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2022, Vol. 35 
