Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability

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... (read more)

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