Policy Actor Critic

Policy Actor-Critic (PAC) methods are a class of reinforcement learning algorithms aiming to efficiently learn optimal policies by simultaneously updating a policy (actor) and a value function (critic). Current research focuses on improving sample efficiency and robustness of off-policy PAC algorithms, exploring techniques like multi-step learning, pessimism/optimism control, and unique experience replay to optimize data usage and mitigate overestimation bias. These advancements are significant for addressing challenges in continuous control tasks and enabling applications in robotics, autonomous driving, and other domains requiring efficient learning from complex, high-dimensional environments.