This is the ordinary importance sampling, because we use a simple average. We wish to estimate $v_(s)$ we simply scale the returns by the ratios and average the results:.As more returns are observed, this value should converge to the expected value. In this part we focus on learning the state-value function for a given policy.Īn obvious way to estimate it from experience is to average the returns observed after visits to that state. Instead of computing the value function from our knowledge of the MDP, we learn it from sample returns. They are incremental in the episode-by-episode sense (not in a step-by-step sense).Īs in Dynamic Programming, we adapt the idea of general policy iteration. Monte Carlo methods are based on episodes and averaging sample returns. Here we don’t have any knowledge of the environment dynamics, we learn only by experience. ![]() ![]() Off-policy Prediction by Importance Samplingįirst learning method for estimating value functions and discovering optimal policies. Monte Carlo control without Exploring Starts This post is part of the Sutton & Barto summary series.
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