Experience Replay Method with Attention for Multi-agent Reinforcement Learning
摘要
To enhance the efficiency of the experience replay method, this article proposes an improvement by incorporating the past experience reward value and the timing difference error (TD error) to form a prioritized R-T experience parameter. Additionally, an attention mechanism is introduced to determine data priority based on the R-T experience parameter. This improved experience replay method is then applied to the multi-agent deep deterministic policy gradient algorithm, resulting in improved algorithm training efficiency and stability.