Evolutionary Policy Search

Evolutionary policy search uses evolutionary algorithms to optimize policies in reinforcement learning, aiming to find optimal strategies without relying on gradient information. Current research focuses on improving exploration efficiency through techniques like incorporating behavior descriptors to guide the search, developing more efficient neural network encodings, and leveraging intrinsic motivation to encourage diverse and rewarding behaviors. These advancements enhance the robustness and scalability of evolutionary methods, particularly in complex, high-dimensional, and reward-sparse environments, with applications spanning robotics and control systems.