Offline Goal Conditioned Reinforcement Learning

Offline goal-conditioned reinforcement learning (GCRL) focuses on training robots to achieve various goals using only pre-collected data, eliminating the need for extensive online interaction. Current research emphasizes improving the learning of optimal policies from often suboptimal or sparse offline datasets, exploring techniques like metric learning to better represent value functions, conditional diffusion models for trajectory generation, and novel reward shaping methods. These advancements are significant because they enable the development of more robust and versatile robotic agents capable of performing complex tasks in real-world scenarios with limited data and without extensive manual reward engineering.

Papers