arXiv:2510.03508v3 Announce Type: replace 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
The continuous drive to improve reinforcement learning algorithms for complex, real-world applications motivates ongoing research into more stable and effective training methods.
This development proposes a more stable and efficient method for training expressive diffusion policies, which could significantly accelerate progress in AI agent development and autonomous systems.
The proposed D2AC algorithm offers a new approach to policy improvement and critic design in RL, potentially reducing variance and improving the robustness of online learning for diffusion policies.
- · AI research institutions
- · Robotics companies
- · Generative AI developers
- · Autonomous systems sector
- · Traditional RL algorithm developers
More sophisticated and reliable AI agents can be developed and deployed in diverse applications.
Accelerated development of AI-powered automation across industries, potentially impacting workforce structures.
Increased competition among companies to integrate and leverage advanced AI agents for efficiency gains and new product offerings.
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