Aidan Scannell
Aidan Scannell
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Mode-Constrained Exploration for Model-Based Reinforcement Learning
This work presents a learning-based control method for navigating to a target state in unknown, or partially unknown, multimodal dynamical systems. In particular, it develops a model-based reinforcement learning algorithm that can remain in a desired dynamics mode with high probability. For example, if some of the dynamics modes are believed to be inoperable.
Aidan Scannell
,
Carl Henrik Ek
,
Arthur Richards
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Identifiable Mixtures of Sparse Variational Gaussian Process Experts
Mixture models are inherently unidentifiable as different combinations of component distributions and mixture weights can generate the …
Aidan Scannell
,
Carl Henrik Ek
,
Arthur Richards
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Identifiable Mixtures of Sparse Variational Gaussian Process Experts
This work introduces a variational lower bound for the Mixture of Gaussian Process Experts model with a GP-based gating network based on sparse GPs. The model (and inference) are implemented in GPflow/TensorFlow.
Aidan Scannell
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