Physic Based Reward

Physics-based reward functions are increasingly used in reinforcement learning to guide agents towards solutions that respect physical constraints and properties, improving the efficiency and safety of learned behaviors. Current research focuses on integrating physics-based rewards into diverse applications, including robot manipulation, image analysis in microscopy, and wireless navigation, often employing reinforcement learning algorithms and leveraging physics simulations. This approach enhances the generalization and robustness of learned policies, leading to more reliable and efficient systems in various domains, from automated material analysis to safer robotic operations. The resulting improvements in sample efficiency and zero-shot generalization are particularly significant.