RL Bite: Monte Carlo Search Tree

Abstract Link to heading Let&rsquo;s talk a bit about Model-Based Reinforcement Learning. The idea is that our RL Agent not just learns a policy to follow or/and a value function (Q function) but also tries to model the environment it is in. This is done by learning the transition dynamics $p(s&rsquo;|s,a)$ (also known as World Model) and a Reward function $\hat{R}(s,a)$. Once we have our world…