Hierarchical Policy Learning
Hierarchical policy learning aims to solve complex tasks by breaking them down into simpler sub-tasks, managed by a hierarchy of policies. Current research focuses on improving subgoal reachability through bidirectional information sharing between policy levels, employing various architectures like options frameworks and model predictive control, and addressing challenges such as hallucination in vision-language models and efficient learning from limited data in offline settings. This approach enhances the robustness, efficiency, and generalizability of reinforcement learning agents, with significant implications for robotics, autonomous systems, and other domains requiring complex sequential decision-making.
Papers
Hierarchical Subspaces of Policies for Continual Offline Reinforcement Learning
Anthony Kobanda, Rémy Portelas, Odalric-Ambrym Maillard, Ludovic Denoyer
Simulation-Free Hierarchical Latent Policy Planning for Proactive Dialogues
Tao He, Lizi Liao, Yixin Cao, Yuanxing Liu, Yiheng Sun, Zerui Chen, Ming Liu, Bing Qin