Abstract
Reinforcement learning in real-world environments often suffers from severe performance degradation due to delayed feedback. Existing approaches typically mitigate performance degradation caused by observation delays by constructing augmented states or predicting the true states. However, these methods often overlook the inherent discrepancy between delayed state and true states induced by stochastic MDP. We theoretically prove the existence of such a discrepancy and show that it leads to the degradation of the optimal policy.
To address this challenge, we propose Diffusion Guided Uncertainty Aware Delayed Policy Optimization (DUPO). Our method explicitly models the relationship between delayed state message and the current state using a diffusion model, and leverages the resulting discrepancy estimates to weight delayed policies. Extensive experiments on continuous robotic control tasks with multiple stochastic delays demonstrate that DUPO consistently outperforms existing methods and remains effective even under long and random delay scenarios.
Blogger's Review: The DUPO method effectively addresses the issue of delayed feedback by incorporating diffusion models, providing a fresh perspective for reinforcement learning applications in complex environments. Its theoretical foundation and experimental results indicate strong practicality, making it worth exploring further in real-world applications, particularly in robotic control where its potential is significant.