Action Sampling

Action sampling, a technique used in reinforcement learning, focuses on optimizing how agents select actions, aiming to improve exploration-exploitation balance and learning efficiency. Current research explores adaptive action sampling methods within various reinforcement learning frameworks, including Proximal Policy Optimization (PPO) and off-policy actor-critic algorithms, often incorporating colored noise or dataset constraints to refine action selection. These advancements are improving the performance of reinforcement learning agents across diverse applications, from job shop scheduling to robotic manipulation and dialogue generation, by enhancing sample efficiency and mitigating issues like value overestimation.