DIME:Diffusion-Based Maximum Entropy Reinforcement Learning

• URL: http://arxiv.org/abs/2502.02316v1
• Date: Tue, 04 Feb 2025 13:37:14 GMT
• Title: DIME:Diffusion-Based Maximum Entropy Reinforcement Learning
• Authors: Onur Celik, Zechu Li, Denis Blessing, Ge Li, Daniel Palanicek, Jan Peters, Georgia Chalvatzaki, Gerhard Neumann,
• Abstract summary: Maximum entropy reinforcement learning (MaxEnt-RL) has become the standard approach to RL due to its beneficial exploration properties. We propose Diffusion-Based Maximum Entropy RL (DIME) to overcome the intractability of computing their marginal entropy.
• Score: 37.420420953705396
• License:
• Abstract: Maximum entropy reinforcement learning (MaxEnt-RL) has become the standard approach to RL due to its beneficial exploration properties. Traditionally, policies are parameterized using Gaussian distributions, which significantly limits their representational capacity. Diffusion-based policies offer a more expressive alternative, yet integrating them into MaxEnt-RL poses challenges--primarily due to the intractability of computing their marginal entropy. To overcome this, we propose Diffusion-Based Maximum Entropy RL (DIME). DIME leverages recent advances in approximate inference with diffusion models to derive a lower bound on the maximum entropy objective. Additionally, we propose a policy iteration scheme that provably converges to the optimal diffusion policy. Our method enables the use of expressive diffusion-based policies while retaining the principled exploration benefits of MaxEnt-RL, significantly outperforming other diffusion-based methods on challenging high-dimensional control benchmarks. It is also competitive with state-of-the-art non-diffusion based RL methods while requiring fewer algorithmic design choices and smaller update-to-data ratios, reducing computational complexity.

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