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Mode-constrained Model-based Reinforcement Learning via Gaussian Processes
We present a model-based RL algorithm that constrains training to a single dynamic mode with high probability. This is a difficult problem because the mode constraint is a hidden variable associated with the environment’s dynamics. As such, it is 1) unknown a priori and 2) we do not observe its output from the environment, so cannot learn it with supervised learning.
Aidan Scannell, Carl Henrik Ek, Arthur Richards
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Mode-constrained Model-based Reinforcement Learning via Gaussian Processes
Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation
Synergising Bayesian inference and Riemannian geometry for control in multimodal dynamical systems.
Aidan Scannell, Carl Henrik Ek, Arthur Richards
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Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation