Compared to on-policy policy gradient techniques, off-policy model-free deep reinforcement learning (RL) approaches that use previously gathered data can improve sampling efficiency. However, off-policy learning becomes challenging when the discrepancy between the distributions of the policy of interest and the policies that collected the data increases. Although the well-studied importance sampling and off-policy policy gradient techniques were proposed to compensate for this discrepancy, they usually require a collection of long trajectories that increases the computational complexity and induce additional problems such as vanishing or exploding gradients. Moreover, their generalization to continuous action domains is strictly limited as they require action probabilities, which is unsuitable for deterministic policies. To overcome these limitations, we introduce an alternative off-policy correction algorithm for continuous action spaces, Actor-Critic Off-Policy Correction (AC-Off-POC), to mitigate the potential drawbacks introduced by the previously collected data. Through a novel discrepancy measure computed by the agent's most recent action decisions on the states of the randomly sampled batch of transitions, the approach does not require actual or estimated action probabilities for any policy and offers an adequate one-step importance sampling. Theoretical results show that the introduced approach can achieve a contraction mapping with a fixed unique point, which allows a "safe" off-policy learning. Our empirical results suggest that AC-Off-POC consistently improves the state-of-the-art and attains higher returns in fewer steps than the competing methods by efficiently scheduling the learning rate in Q-learning and policy optimization.

本文研究了基于离线数据的深度强化学习算法，提出了一种新的策略相似度度量方法来提高算法的采样效率和泛化能力，并且证明了该方法可以实现安全的离线学习。实验证明，该方法相较于其他竞争算法在大多数情况下能够更高效地提高学习效率。

无需重要性采样的Actor-Critic方法的离线校正