Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning

Accurate value estimates are important for off-policy reinforcement learning. Algorithms based on temporal difference learning typically are prone to an over- or underestimation bias building up over time. In this letter, we propose a general method called Adaptively Calibrated Critics (ACC) that uses the most recent high variance but unbiased on-policy rollouts to alleviate the bias of the low variance temporal difference targets. We apply ACC to Truncated Quantile Critics [1], which is an algorithm for continuous control that allows regulation of the bias with a hyperparameter tuned per environment. The resulting algorithm adaptively adjusts the parameter during training rendering hyperparameter search unnecessary and sets a new state of the art on the OpenAI gym continuous control benchmark among all algorithms that do not tune hyperparameters for each environment. ACC further achieves improved results on different tasks from the Meta-World robot benchmark. Additionally, we demonstrate the generality of ACC by applying it to TD3 [2] and showing an improved performance also in this setting.

Automated summary

This letter proposes a method called Adaptively Calibrated Critics (ACC) to alleviate the bias of low variance temporal difference targets by using recent high variance but unbiased on-policy rollouts. ACC is applied to Truncated Quantile Critics algorithm to regulate the bias with a hyperparameter. ACC achieves state-of-the-art results on the OpenAI gym continuous control benchmark and demonstrates improved performance on various tasks from the Meta-World robot benchmark.