Manifold Embeddings for Model-Based Reinforcement Learning of Neurostimulation Policies

Real-world reinforcement learning problems often exhibit nonlinear, continuous-valued, noisy, partially-observable state-spaces that are prohibitively expensive to explore. The formal reinforcement