Multi-Agent Trust Region Policy Optimization

• URL: http://arxiv.org/abs/2010.07916v3
• Date: Sat, 5 Aug 2023 01:00:50 GMT
• Title: Multi-Agent Trust Region Policy Optimization
• Authors: Hepeng Li and Haibo He
• Abstract summary: We show that the policy update of TRPO can be transformed into a distributed consensus optimization problem for multi-agent cases. We propose a decentralized MARL algorithm, which we call multi-agent TRPO (MATRPO)
• Score: 34.91180300856614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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