Model-Based Reinforcement Learning
Here, we are looking into new probabilistic methods for learning latent world models for model-based reinforcement learning.
We study how recent State Space modeling approaches for Model-Based RL represent uncertainties. We find some flaws and propose a theoretically better-grounded alternative. We show it improves performance in tasks where it is important to appropriately capture uncertainty. If you want to know who this relates to the cat and the hamster you have to read the paper.
more