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We address the issue of estimation bias in deep reinforcement learning (DRL)by introducing solution mechanisms that include a new, twin TD-regularizedactor-critic (TDR) method. It aims at reducing both over and under-estimationerrors. With TDR and by combining good DRL improvements, such as distributionallearning and long N-step surrogate stage reward (LNSS) method, we show that ournew TDR-based actor-critic learning has enabled DRL methods to outperform theirrespective baselines in challenging environments in DeepMind Control Suite.Furthermore, they elevate TD3 and SAC respectively to a level of performancecomparable to that of D4PG (the current SOTA), and they also improve theperformance of D4PG to a new SOTA level measured by mean reward, convergencespeed, learning success rate, and learning variance.

Mitigating Estimation Errors by Twin TD-Regularized Actor and Critic for Deep Reinforcement Learning