Directly Attention Loss Adjusted Prioritized Experience Replay
This is an incremental improvement for reinforcement learning practitioners, addressing a known bottleneck in sample efficiency.
The paper tackles the estimation deviation caused by Prioritized Experience Replay's non-uniform sampling by proposing DALAP, a framework that quantifies distribution shifts using a Parallel Self-Attention network to compensate errors, resulting in improved convergence rate and reduced training variance.
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.