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

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.