Hybrid Reinforcement Learning

Hybrid reinforcement learning (RL) combines offline data with online interaction to improve the efficiency and robustness of RL agents. Current research focuses on developing algorithms that leverage both data sources effectively, often employing model architectures like actor-critic methods and hierarchical RL, and addressing challenges such as limited data coverage and mixed-variable action spaces. This approach holds significant promise for applications ranging from robotics and resource optimization to personalized medicine and AI safety, by enabling faster training, improved generalization, and more reliable performance in complex real-world scenarios.