Policy Optimization

Policy optimization is a core area of reinforcement learning focused on efficiently finding optimal policies, or strategies, for agents interacting with an environment to maximize rewards. Current research emphasizes improving sample efficiency and robustness, particularly through algorithms like Proximal Policy Optimization (PPO) and its variants, as well as exploring new approaches such as Direct Preference Optimization (DPO) and incorporating techniques like diffusion models and dual regularization. These advancements are significant for both theoretical understanding of reinforcement learning and practical applications across diverse fields, including robotics, natural language processing, and resource management.