  The experience replay mechanism allows agents to use the experiences multiple
times. In prior works, the sampling probability of the transitions was adjusted
according to their importance. Reassigning sampling probabilities for every
transition in the replay buffer after each iteration is highly inefficient.
Therefore, experience replay prioritization algorithms recalculate the
significance of a transition when the corresponding transition is sampled to
gain computational efficiency. However, the importance level of the transitions
changes dynamically as the policy and the value function of the agent are
updated. In addition, experience replay stores the transitions are generated by
the previous policies of the agent that may significantly deviate from the most
recent policy of the agent. Higher deviation from the most recent policy of the
agent leads to more off-policy updates, which is detrimental for the agent. In
this paper, we develop a novel algorithm, Batch Prioritizing Experience Replay
via KL Divergence (KLPER), which prioritizes batch of transitions rather than
directly prioritizing each transition. Moreover, to reduce the off-policyness
of the updates, our algorithm selects one batch among a certain number of
batches and forces the agent to learn through the batch that is most likely
generated by the most recent policy of the agent. We combine our algorithm with
Deep Deterministic Policy Gradient and Twin Delayed Deep Deterministic Policy
Gradient and evaluate it on various continuous control tasks. KLPER provides
promising improvements for deep deterministic continuous control algorithms in
terms of sample efficiency, final performance, and stability of the policy
during the training.
