Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability

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Reinforcement Learning in Healthcare: A Survey (2019, methodology): “Bush and Pineau [159] applied manifold embeddings to reconstruct the observable state space in MRL, and applied the proposed approach to tackle the high com-”
Adaptive control of epileptiform excitability in an in vitro model of limbic seizures (2013, unknown): “…stimulation for open-loop (D'Arcangelo, et al., 2005; Durand and Bikson, 2001; Ellis and Stevens, 2008; Schiller and Bankirer, 2007) as well as for closed-loop strategies (Bush and Pineau, 2009; Durand and Bikson, 2001; Nakagawa and Durand, 1991; Schiff, et al., 1994; Schiller and Bankirer, 2007).”
Policy Gradients Incorporating the Future (2021, background): “On the other hand for model-based approaches, learning an accurate dynamics model in such domains is a difficult, potentially ill-defined problem [38, 6].”
Adaptive Control of Epileptic Seizures using Reinforcement Learning (2010, background): “A manifold embedding approach combined with reinforcement learning applied to the same problem (same animal model, same objectives) is still under investigation [14].”