Policy Representation via Diffusion Probability Model for Reinforcement
  Learning
Long Yang Zhixiong Huang Fenghao Lei Yucun Zhong Yiming Yang Cong Fang Shiting Wen Binbin Zhou Zhouchen Lin
Popular reinforcement learning (RL) algorithms tend to produce a unimodal
policy distribution, which weakens the expressiveness of complicated policy and
decays the ability of exploration. The diffusion probability model is powerful
to learn complicated multimodal distributions, which has shown promising and
potential applications to RL. In this paper, we formally build a theoretical
foundation of policy representation via the diffusion probability model and
provide practical implementations of diffusion policy for online model-free RL.
Concretely, we character diffusion policy as a stochastic process, which is a
new approach to representing a policy. Then we present a convergence guarantee
for diffusion policy, which provides a theory to understand the multimodality
of diffusion policy. Furthermore, we propose the DIPO which is an
implementation for model-free online RL with DIffusion POlicy. To the best of
our knowledge, DIPO is the first algorithm to solve model-free online RL
problems with the diffusion model. Finally, extensive empirical results show
the effectiveness and superiority of DIPO on the standard continuous control
Mujoco benchmark.