Implicit Policy

Implicit policies represent a growing area of reinforcement learning research focused on learning control strategies without explicitly defining a policy function. Current work centers on developing efficient algorithms for extracting these implicit policies from learned value functions or other representations, often employing architectures like two-tower networks or integrating techniques from model predictive control and energy-based models. This approach offers advantages in computational efficiency and robustness, particularly in offline reinforcement learning and complex robotic control tasks, leading to improved performance in challenging real-world scenarios.