Diffusion Policy through Conditional Proximal Policy Optimization

• URL: http://arxiv.org/abs/2603.04790v1
• Date: Thu, 05 Mar 2026 04:12:13 GMT
• Title: Diffusion Policy through Conditional Proximal Policy Optimization
• Authors: Ben Liu, Shunpeng Yang, Hua Chen,
• Abstract summary: diffusion policies have shown strong potential in modeling multi-modal behaviors.<n>A key challenge is the difficulty of computing action log-likelihood under the diffusion model.<n>We propose a novel and efficient method to train a diffusion policy in an on-policy setting.
• Score: 6.836651088754774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action Spaces [57.466101098183884]
  Reinforcement learning (RL) struggles to scale to large, action spaces common in many real-world problems.<n>This paper introduces a novel framework for training discrete diffusion models as highly effective policies in complex settings.
  arXiv  Detail & Related papers  (2025-09-26T21:53:36Z)
• One-Step Flow Policy Mirror Descent [52.31612487608593]
  Flow Policy Mirror Descent (FPMD) is an online RL algorithm that enables 1-step sampling during flow policy inference.<n>Our approach exploits a theoretical connection between the distribution variance and the discretization error of single-step sampling in straight flow matching models.
  arXiv  Detail & Related papers  (2025-07-31T15:51:10Z)
• Learning Multimodal Behaviors from Scratch with Diffusion Policy Gradient [26.675822002049372]
  Deep Diffusion Policy Gradient (DDiffPG) is a novel actor-critic algorithm that learns from scratch multimodal policies. DDiffPG forms a multimodal training batch and utilizes mode-specific Q-learning to mitigate the inherent greediness of the RL objective. Our approach further allows the policy to be conditioned on mode-specific embeddings to explicitly control the learned modes.
  arXiv  Detail & Related papers  (2024-06-02T09:32:28Z)
• Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization [55.97310586039358]
  Diffusion models have garnered widespread attention in Reinforcement Learning (RL) for their powerful expressiveness and multimodality.<n>We propose a novel model-free diffusion-based online RL algorithm, Q-weighted Variational Policy Optimization (QVPO)<n>Specifically, we introduce the Q-weighted variational loss, which can be proved to be a tight lower bound of the policy objective in online RL under certain conditions.<n>We also develop an efficient behavior policy to enhance sample efficiency by reducing the variance of the diffusion policy during online interactions.
  arXiv  Detail & Related papers  (2024-05-25T10:45:46Z)
• Diffusion Actor-Critic with Entropy Regulator [32.79341490514616]
  We propose an online RL algorithm termed diffusion actor-critic with entropy regulator (DACER)<n>This algorithm conceptualizes the reverse process of the diffusion model as a novel policy function.<n>Experiments on MuJoCo benchmarks and a multimodal task demonstrate that the DACER algorithm achieves state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2024-05-24T03:23:27Z)
• Don't Start from Scratch: Behavioral Refinement via Interpolant-based Policy Diffusion [16.44141792109178]
  Diffusion models learn to shape a policy by diffusing actions (or states) from standard Gaussian noise. The target policy to be learned is often significantly different from Gaussian and this can result in poor performance when using a small number of diffusion steps. We contribute both theoretical results, a new method, and empirical findings that show the benefits of using an informative source policy.
  arXiv  Detail & Related papers  (2024-02-25T12:19:21Z)
• Policy Representation via Diffusion Probability Model for Reinforcement Learning [67.56363353547775]
  We build a theoretical foundation of policy representation via the diffusion probability model. We present a convergence guarantee for diffusion policy, which provides a theory to understand the multimodality of diffusion policy. We propose the DIPO which is an implementation for model-free online RL with DIffusion POlicy.
  arXiv  Detail & Related papers  (2023-05-22T15:23:41Z)
• Policy Dispersion in Non-Markovian Environment [53.05904889617441]
  This paper tries to learn the diverse policies from the history of state-action pairs under a non-Markovian environment. We first adopt a transformer-based method to learn policy embeddings. Then, we stack the policy embeddings to construct a dispersion matrix to induce a set of diverse policies.
  arXiv  Detail & Related papers  (2023-02-28T11:58:39Z)
• Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning [70.20191211010847]
  Offline reinforcement learning (RL) aims to learn an optimal policy using a previously collected static dataset. We introduce Diffusion Q-learning (Diffusion-QL) that utilizes a conditional diffusion model to represent the policy. We show that our method can achieve state-of-the-art performance on the majority of the D4RL benchmark tasks.
  arXiv  Detail & Related papers  (2022-08-12T09:54:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.