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Learning On the Go: Temporal-Difference Learning in Reinforcement Learning

This blog explores the concept of Temporal-Difference Learning in Reinforcement Learning, blending the strengths of Monte Carlo methods and Dynamic Programming for faster, more flexible learning.

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Published on Tue Apr 29 2025

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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.

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Updated on Thu Apr 17 2025