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Compared to on-policy counterparts, off-policy model-free deep reinforcementlearning can improve data efficiency by repeatedly using the previouslygathered data. However, off-policy learning becomes challenging when thediscrepancy between the underlying distributions of the agent's policy andcollected data increases. Although the well-studied importance sampling andoff-policy policy gradient techniques were proposed to compensate for thisdiscrepancy, they usually require a collection of long trajectories and induceadditional problems such as vanishing/exploding gradients or discarding manyuseful experiences, which eventually increases the computational complexity.Moreover, their generalization to either continuous action domains or policiesapproximated by deterministic deep neural networks is strictly limited. Toovercome these limitations, we introduce a novel policy similarity measure tomitigate the effects of such discrepancy in continuous control. Our methodoffers an adequate single-step off-policy correction that is applicable todeterministic policy networks. Theoretical and empirical studies demonstratethat it can achieve a "safe" off-policy learning and substantially improve thestate-of-the-art by attaining higher returns in fewer steps than the competingmethods through an effective schedule of the learning rate in Q-learning andpolicy optimization.

Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach