Augmented-Ensemble TD3: Overcoming the Shackles of Constant Action Delay

Reinforcement Learning has experienced significant advances in various domains. However, delayed feedback in RL environments poses challenges due to the violation of the Markovian property. In this paper, we propose an approach to address the issues of Markov Decision Process(MDP) with delayed feedback. The proposed approach, called “Augmented-Ensemble Twin-Delayed Deep Deterministic Policy Gradient(TD3),” aims to mitigate the performance degradation caused by delayed feedback.