Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information. (arXiv:2211.00164v1 [cs.LG])

Learning to control an agent from data collected offline in a rich
pixel-based visual observation space is vital for real-world applications of
reinforcement learning (RL). A major challenge in this setting is the presence
of input information that is hard to model and irrelevant to controlling the
agent. This problem has been approached by the theoretical RL community through
the lens of exogenous information, i.e, any control-irrelevant information
contained in observations. For example, a robot navigating in busy streets
needs …

arxiv exogenous information offline