Mode-Constrained Exploration for Model-Based Reinforcement Learning

Aidan Scannell
Sep 24, 2022
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reinforcement-learning machine-learning gaussian-processes optimal-control robotics python TensorFlow GPflow research
Aidan Scannell
Aidan Scannell
Postdoctoral Researcher

My research interests include model-based reinforcement learning, probabilistic machine learning (gaussian processes, Bayesian neural networks, approximate Bayesian inference, etc), learning-based control and optimal control.

Publications

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

Events

Model-based reinforcement learning under uncertainty

In this talk I’ll present our recent paper that has just been accepted into AISTATS 2023, titled Mode-Constrained Model-Based …

Feb 13, 2023 11:30 AM — 12:30 PM University of Cambridge
Aidan Scannell
Project Slides
Model-based reinforcement learning under uncertainty