MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2106.11652v1
• Date: Tue, 22 Jun 2021 10:21:00 GMT
• Title: MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning
• Authors: Zhiwei Xu, Dapeng Li, Yunpeng Bai, Guoliang Fan
• Abstract summary: MMD-mix is a method that combines distributional reinforcement learning and value decomposition. The experiments demonstrate that MMD-mix outperforms prior baselines in the Star Multi-Agent Challenge (SMAC) environment.
• Score: 15.972363414919279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the real world, many tasks require multiple agents to cooperate with each other under the condition of local observations. To solve such problems, many multi-agent reinforcement learning methods based on Centralized Training with Decentralized Execution have been proposed. One representative class of work is value decomposition, which decomposes the global joint Q-value $Q_\text{jt}$ into individual Q-values $Q_a$ to guide individuals' behaviors, e.g. VDN (Value-Decomposition Networks) and QMIX. However, these baselines often ignore the randomness in the situation. We propose MMD-MIX, a method that combines distributional reinforcement learning and value decomposition to alleviate the above weaknesses. Besides, to improve data sampling efficiency, we were inspired by REM (Random Ensemble Mixture) which is a robust RL algorithm to explicitly introduce randomness into the MMD-MIX. The experiments demonstrate that MMD-MIX outperforms prior baselines in the StarCraft Multi-Agent Challenge (SMAC) environment.

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