適応的に校正された批評家推定による深層強化学習
Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning
オンポリシーの分散は大きいが偏りのないロールアウトを用いてTDターゲットのバイアスを適応的に補正するACCを提案し、連続制御ベンチマークで環境ごとのハイパーパラメータ調整なしに最先端性能を達成した。
著者: Nicolai Dorka, Tim Welschehold, Joschka Boedecker, Wolfram Burgard
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
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.
