D2 Actor Critic: Diffusion Actor Meets Distributional Critic

• URL: http://arxiv.org/abs/2510.03508v2
• Date: Tue, 14 Oct 2025 18:08:10 GMT
• Title: D2 Actor Critic: Diffusion Actor Meets Distributional Critic
• Authors: Lunjun Zhang, Shuo Han, Hanrui Lyu, Bradly C Stadie,
• Abstract summary: We introduce D2AC, a new model-free reinforcement learning (RL) algorithm designed to train expressive diffusion policies online effectively.<n>At its core is a policy improvement objective that avoids the high variance of typical policy gradients and the complexity of backpropagation through time.<n>This stable learning process is critically enabled by our second contribution: a robust distributional critic, which we design through a fusion of distributional RL and clipped double Q-learning.<n>The resulting algorithm is highly effective, achieving state-of-the-art performance on a benchmark of eighteen hard RL tasks, including Humanoid, Dog, and Shadow Hand
• Score: 4.669386607943427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce D2AC, a new model-free reinforcement learning (RL) algorithm designed to train expressive diffusion policies online effectively. At its core is a policy improvement objective that avoids the high variance of typical policy gradients and the complexity of backpropagation through time. This stable learning process is critically enabled by our second contribution: a robust distributional critic, which we design through a fusion of distributional RL and clipped double Q-learning. The resulting algorithm is highly effective, achieving state-of-the-art performance on a benchmark of eighteen hard RL tasks, including Humanoid, Dog, and Shadow Hand domains, spanning both dense-reward and goal-conditioned RL scenarios. Beyond standard benchmarks, we also evaluate a biologically motivated predator-prey task to examine the behavioral robustness and generalization capacity of our approach.

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