
Learning by Doing: Monte Carlo Methods in Reinforcement Learning
This post explores **Monte Carlo methods** in reinforcement learning — a class of algorithms that learn by averaging returns after complete episodes of experience. We break down how agents can evaluate and improve policies using only sampled trajectories, without knowing the environment’s dynamics.
Updated on Thu Apr 17 2025