Back Again to Monte Carlo

Author(s): Rem E

Originally published on Towards AI.

We will explore our second method for solving RL problems

We’re diving into our second method for solving RL problems: Monte Carlo (MC). You’ve already seen it in Implementing the Value Function the Monte Carlo Way (tutorial 5). We computed values but didn’t update the policy. Now we’ll do it properly, step by step: generate complete episodes, estimate returns, average them to get value estimates, and then use those estimates to improve the policy

This article introduces the Monte Carlo (MC) method for solving reinforcement learning (RL) problems, focusing on generating complete episodes and estimating returns to improve policies. It contrasts MC with Dynamic Programming (DP), highlighting how MC only requires experience from interactions, unlike DP, which demands full knowledge of the environment. The article details two methods within MC: Every-visit MC and First-visit MC, and emphasizes the importance of exploration versus exploitation through on-policy and off-policy methods. It concludes with a discussion about ε-greedy policies and their role in ensuring continuous exploration while gradually shifting towards optimal policies.

Read the full blog for free on Medium.

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Published via Towards AI