Policy Policy Gradient

Policy gradient methods in reinforcement learning aim to optimize an agent's policy by iteratively adjusting its parameters based on the gradient of a performance objective. Current research heavily focuses on improving off-policy policy gradient algorithms, which learn from data collected under a different policy than the one being optimized, addressing challenges like high variance and bias through techniques such as optimal baselines, importance sampling corrections, and novel actor-critic architectures. These advancements enhance sample efficiency and robustness, leading to improved performance in various applications, including robotics and large language model fine-tuning. The development of theoretically sound and practically efficient off-policy methods is a significant area of ongoing investigation.