<ul><li>Back-stepping experience replay (BER) is a reinforcement learning technique that can accelerate learning efficiency in reversible environments.</li><li>An enhanced version called Generalized BER (GBER) is proposed, which extends the original algorithm to sparse-reward environments.</li><li>GBER improves the performance of BER by introducing relabeling mechanism and applying diverse sampling strategies.</li><li>Experimental results show that GBER significantly boosts the performance and stability of the baseline algorithm in various sparse-reward environments.</li></ul>

Generalized Back-Stepping Experience Replay in Sparse-Reward Environments

Discover more