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arXiv · 2010.07916

Multi-Agent Trust Region Policy Optimization

Hepeng Li·Haibo He
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Abstract

We extend trust region policy optimization (TRPO) to multi-agent reinforcement learning (MARL) problems. We show that the policy update of TRPO can be transformed into a distributed consensus optimization problem for multi-agent cases. By making a series of approximations to the consensus optimization model, we propose a decentralized MARL algorithm, which we call multi-agent TRPO (MATRPO). This algorithm can optimize distributed policies based on local observations and private rewards. The agents do not need to know observations, rewards, policies or value/action-value functions of other agents. The agents only share a likelihood ratio with their neighbors during the training process. The algorithm is fully decentralized and privacy-preserving. Our experiments on two cooperative games demonstrate its robust performance on complicated MARL tasks.

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Hepeng Li, Haibo He. 2020-10-15. Multi-Agent Trust Region Policy Optimization. https://arxiv.org/abs/2010.07916

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