Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. (arXiv:2206.04282v1 [cs.LG])

In real-world reinforcement learning applications the learner's observation
space is ubiquitously high-dimensional with both relevant and irrelevant
information about the task at hand. Learning from high-dimensional observations
has been the subject of extensive investigation in supervised learning and
statistics (e.g., via sparsity), but analogous issues in reinforcement learning
are not well understood, even in finite state/action (tabular) domains. We
introduce a new problem setting for reinforcement learning, the Exogenous
Markov Decision Process (ExoMDP), in which the state space admits an …

arxiv exogenous information learning lg reinforcement reinforcement learning