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The experience replay mechanism allows agents to use the experiences multipletimes. In prior works, the sampling probability of the transitions was adjustedaccording to their importance. Reassigning sampling probabilities for everytransition in the replay buffer after each iteration is highly inefficient.Therefore, experience replay prioritization algorithms recalculate thesignificance of a transition when the corresponding transition is sampled togain computational efficiency. However, the importance level of the transitionschanges dynamically as the policy and the value function of the agent areupdated. In addition, experience replay stores the transitions are generated bythe previous policies of the agent that may significantly deviate from the mostrecent policy of the agent. Higher deviation from the most recent policy of theagent leads to more off-policy updates, which is detrimental for the agent. Inthis paper, we develop a novel algorithm, Batch Prioritizing Experience Replayvia KL Divergence (KLPER), which prioritizes batch of transitions rather thandirectly prioritizing each transition. Moreover, to reduce the off-policynessof the updates, our algorithm selects one batch among a certain number ofbatches and forces the agent to learn through the batch that is most likelygenerated by the most recent policy of the agent. We combine our algorithm withDeep Deterministic Policy Gradient and Twin Delayed Deep Deterministic PolicyGradient and evaluate it on various continuous control tasks. KLPER providespromising improvements for deep deterministic continuous control algorithms interms of sample efficiency, final performance, and stability of the policyduring the training.

Off-Policy Correction for Deep Deterministic Policy Gradient Algorithms via Batch Prioritized Experience Replay