Off-Policy Correction For Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2111.11229v3
• Date: Wed, 3 Apr 2024 17:13:05 GMT
• Title: Off-Policy Correction For Multi-Agent Reinforcement Learning
• Authors: Michał Zawalski, Błażej Osiński, Henryk Michalewski, Piotr Miłoś,
• Abstract summary: Multi-agent reinforcement learning (MARL) provides a framework for problems involving multiple interacting agents. Despite apparent similarity to the single-agent case, multi-agent problems are often harder to train and analyze theoretically. We propose MA-Trace, a new on-policy actor-critic algorithm, which extends V-Trace to the MARL setting.
• Score: 9.599347559588216
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-agent reinforcement learning (MARL) provides a framework for problems involving multiple interacting agents. Despite apparent similarity to the single-agent case, multi-agent problems are often harder to train and analyze theoretically. In this work, we propose MA-Trace, a new on-policy actor-critic algorithm, which extends V-Trace to the MARL setting. The key advantage of our algorithm is its high scalability in a multi-worker setting. To this end, MA-Trace utilizes importance sampling as an off-policy correction method, which allows distributing the computations with no impact on the quality of training. Furthermore, our algorithm is theoretically grounded - we prove a fixed-point theorem that guarantees convergence. We evaluate the algorithm extensively on the StarCraft Multi-Agent Challenge, a standard benchmark for multi-agent algorithms. MA-Trace achieves high performance on all its tasks and exceeds state-of-the-art results on some of them.

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