DRL Algorithm

Deep reinforcement learning (DRL) algorithms aim to train agents to make optimal sequential decisions in complex environments by learning from experience. Current research focuses on improving DRL's robustness, sample efficiency, and interpretability, often employing model architectures like actor-critic methods and incorporating techniques such as conservative critics, reward shaping, and policy distillation. These advancements are driving applications in diverse fields, including autonomous driving, resource management in wireless networks, and power system optimization, where DRL's ability to learn optimal control policies in dynamic settings offers significant advantages over traditional methods.