Offline reinforcement learning (RL) aims to learn optimal policies from
offline datasets, where the parameterization of policies is crucial but often
overlooked. Recently, Diffsuion-QL significantly boosts the performance of
offline RL by representing a policy with a diffusion model, whose success
relies on a parametrized Markov Chain with hundreds of steps for sampling.
However, Diffusion-QL suffers from two critical limitations. 1) It is
computationally inefficient to forward and backward through the whole Markov
chain during training. 2) It is incompatible with maximum likelihood-based RL
algorithms (e.g., policy gradient methods) as the likelihood of diffusion
models is intractable. Therefore, we propose efficient diffusion policy (EDP)
to overcome these two challenges. EDP approximately constructs actions from
corrupted ones at training to avoid running the sampling chain. We conduct
extensive experiments on the D4RL benchmark. The results show that EDP can
reduce the diffusion policy training time from 5 days to 5 hours on
gym-locomotion tasks. Moreover, we show that EDP is compatible with various
offline RL algorithms (TD3, CRR, and IQL) and achieves new state-of-the-art on
D4RL by large margins over previous methods. Our code is available at
this https URL

该论文提出了一种有效的扩散策略（EDP），用于在线学习优化策略，可以解决传统 Diffusion-QL 的训练效率低和与基于最大似然的 RL 算法不兼容的问题。研究表明，EDP 可将扩散策略的训练时间缩短至 5 小时，在 D4RL 基准测试中实现了新的最先进结果。