A short introduction to model-based Reinforcement Learning

Model-based RL learns a deterministic or probabilistic forward model of the dynamics of an environment for action planning or direct policy optimisation. This knowledge enables agents to learn with up to orders of magnitude fewer samples than their model-free counterparts. The cost however is lower maximal performance, due to model misspecification.

In this seminar we go through the basic approaches of model-based RL following Sergey Levine’s excellent lecture on the subject.