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Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability
2009
Neural Information Processing Systems
Interesting real-world datasets often exhibit nonlinear, noisy, continuous-valued states that are unexplorable, are poorly described by first principles, and are only partially observable. If partial observability can be overcome, these constraints suggest the use of model-based reinforcement learning. We experiment with manifold embeddings to reconstruct the observable state-space in the context of offline, model-based reinforcement learning. We demonstrate that the embedding of a system can
dblp:conf/nips/BushP09
fatcat:orqjthixyvf4lkkwbnknii6v6i