Academic

Diffusion Policy through Conditional Proximal Policy Optimization

arXiv:2603.04790v1 Announce Type: new Abstract: Reinforcement learning (RL) has been extensively employed in a wide range of decision-making problems, such as games and robotics. Recently, diffusion policies have shown strong potential in modeling multi-modal behaviors, enabling more diverse and flexible action generation compared to the conventional Gaussian policy. Despite various attempts to combine RL with diffusion, a key challenge is the difficulty of computing action log-likelihood under the diffusion model. This greatly hinders the direct application of diffusion policies in on-policy reinforcement learning. Most existing methods calculate or approximate the log-likelihood through the entire denoising process in the diffusion model, which can be memory- and computationally inefficient. To overcome this challenge, we propose a novel and efficient method to train a diffusion policy in an on-policy setting that requires only evaluating a simple Gaussian probability. This is achie

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Ben Liu, Shunpeng Yang, Hua Chen · · 1 min read · 10 views
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arXiv:2603.04790v1 Announce Type: new Abstract: Reinforcement learning (RL) has been extensively employed in a wide range of decision-making problems, such as games and robotics. Recently, diffusion policies have shown strong potential in modeling multi-modal behaviors, enabling more diverse and flexible action generation compared to the conventional Gaussian policy. Despite various attempts to combine RL with diffusion, a key challenge is the difficulty of computing action log-likelihood under the diffusion model. This greatly hinders the direct application of diffusion policies in on-policy reinforcement learning. Most existing methods calculate or approximate the log-likelihood through the entire denoising process in the diffusion model, which can be memory- and computationally inefficient. To overcome this challenge, we propose a novel and efficient method to train a diffusion policy in an on-policy setting that requires only evaluating a simple Gaussian probability. This is achieved by aligning the policy iteration with the diffusion process, which is a distinct paradigm compared to previous work. Moreover, our formulation can naturally handle entropy regularization, which is often difficult to incorporate into diffusion policies. Experiments demonstrate that the proposed method produces multimodal policy behaviors and achieves superior performance on a variety of benchmark tasks in both IsaacLab and MuJoCo Playground.

Executive Summary

This article proposes a novel method for training diffusion policies in on-policy reinforcement learning, overcoming the challenge of computing action log-likelihood under the diffusion model. The proposed approach aligns policy iteration with the diffusion process, allowing for efficient computation of action probabilities and natural incorporation of entropy regularization. Experimental results demonstrate the effectiveness of the method in producing multimodal policy behaviors and achieving superior performance on various benchmark tasks.

Key Points

  • ▸ Introduction of a novel method for training diffusion policies in on-policy reinforcement learning
  • ▸ Alignment of policy iteration with the diffusion process for efficient computation of action probabilities
  • ▸ Natural incorporation of entropy regularization into the diffusion policy framework

Merits

Efficient Computation

The proposed method enables efficient computation of action probabilities, reducing the computational and memory requirements compared to existing approaches.

Multimodal Policy Behaviors

The method produces multimodal policy behaviors, allowing for more diverse and flexible action generation in decision-making problems.

Demerits

Limited Theoretical Analysis

The article focuses primarily on the empirical evaluation of the proposed method, with limited theoretical analysis of its convergence properties and stability.

Expert Commentary

The proposed method represents a significant advance in the development of diffusion policies for on-policy reinforcement learning. By aligning policy iteration with the diffusion process, the authors have overcome a key challenge in computing action log-likelihood, enabling efficient computation of action probabilities and natural incorporation of entropy regularization. The experimental results demonstrate the effectiveness of the method in producing multimodal policy behaviors and achieving superior performance on various benchmark tasks. However, further theoretical analysis is necessary to fully understand the convergence properties and stability of the proposed method.

Recommendations

  • ✓ Further theoretical analysis of the proposed method to establish its convergence properties and stability
  • ✓ Extension of the method to more complex decision-making problems, such as multi-agent systems and partially observable environments

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