Reinforcement Learning Algorithms with Python

Policy evaluation

We just saw how using real experience to estimate the value function is an easy process. It is about running the policy in an environment until a final state is reached, then computing the return value and averaging the sampled return, as can be seen in equation (1):

Thus the expected return of a state can be approximated from the experience by averaging the sampling episodes from that state. The methods that estimate the return function using (1) are called Monte Carlo methods. Until all of the state-action pairs are visited and enough trajectory has been sampled, Monte Carlo methods guarantee convergence to the optimal ...