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Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning
Offline reinforcement learning (RL) allows learning sequential behavior from fixed datasets. Since offline datasets do not cover all …
Mohammadreza Nakhaei, Aidan Scannell, Joni Pajarinen
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Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning
Function-space Parameterization of Neural Networks for Sequential Learning
Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining …
Aidan Scannell, Riccardo Mereu, Paul Chang, Ella Tamir, Joni Pajarinen, Arno Solin
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Function-space Parameterization of Neural Networks for Sequential Learning
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