Investigating Bayesian Neural Network Dynamics Models for Model-Based Reinforcement Learning

Nov 19, 2022
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This project seeks to evaluate and compare different approaches for learning dynamics models in model-based RL. In particular, we plan to compare different approximate inference techniques (e.g. Laplace approximation, MC dropout, variational inference), as well as ensemble methods, to understand why they either succeed or fail in different environments.

model-based-reinforcement-learning reinforcement-learning machine-learning bayesian-neural-networks gaussian-processes 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.