Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning
Keywords: Offline Multi-Agent Reinforcement Learning; Diffusion Probabilistic Model
TL;DR: We propose a novel diffusion-based policy into offline MARL setting and show superior performance compared to state-of-the-art algorithms.
Abstract: We present a novel Diffusion Offline Multi-agent Model (DOM2) for offline Multi-Agent Reinforcement Learning (MARL). Different from existing algorithms that rely mainly on conservatism in policy design, DOM2 enhances policy expressiveness and diversity based on diffusion model. Specifically, we incorporate a diffusion model into the policy network and propose a trajectory-based data-augmentation scheme in training. These key ingredients make our algorithm more robust to environment changes and achieve significant improvements in performance, generalization and data-efficiency. Our extensive experimental results demonstrate that DOM2 outperforms existing state-of-the-art methods in all multi-agent particle and multi-agent MuJoCo environments, and generalizes significantly better to shifted environments (in $28$ out of $30$ settings evaluated) thanks to its high expressiveness and diversity. Moreover, DOM2 is ultra data efficient and requires no more than $5\%$ data for achieving the same performance compared to existing algorithms (a $20\times$ improvement in data efficiency).
Supplementary Material: pdf
Primary Area: reinforcement learning
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Submission Number: 2375
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