Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning

TL;DR

This paper introduces Adaptively Calibrated Critics (ACC), a method that dynamically adjusts value estimates in deep reinforcement learning to reduce bias, leading to improved performance across various benchmarks without extensive hyperparameter tuning.

Contribution

The paper proposes a novel ACC method that adaptively calibrates critic estimates using recent on-policy rollouts, eliminating the need for hyperparameter search and achieving state-of-the-art results.

Findings

ACC sets a new state of the art on OpenAI gym benchmarks.

ACC improves performance on Meta-World robot tasks.

Applying ACC to TD3 enhances its effectiveness.

Abstract

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 paper, 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, 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 and showing an improved performance also in this setting.