  Prioritized Experience Replay (PER) enables the model to learn more about
relatively important samples by artificially changing their accessed
frequencies. However, this non-uniform sampling method shifts the state-action
distribution that is originally used to estimate Q-value functions, which
brings about the estimation deviation. In this article, an novel off policy
reinforcement learning training framework called Directly Attention Loss
Adjusted Prioritized Experience Replay (DALAP) is proposed, which can directly
quantify the changed extent of the shifted distribution through Parallel
Self-Attention network, so as to accurately compensate the error. In addition,
a Priority-Encouragement mechanism is designed simultaneously to optimize the
sample screening criterion, and further improve the training efficiency. In
order to verify the effectiveness and generality of DALAP, we integrate it with
the value-function based, the policy-gradient based and multi-agent
reinforcement learning algorithm, respectively. The multiple groups of
comparative experiments show that DALAP has the significant advantages of both
improving the convergence rate and reducing the training variance.
