Monte Carlo Methods | Reinforcement Learning Phase | Reasoning LLMs from Scratch

Monte-Carlo Methods is our first “learning” method which we cover in the Reinforcement Learning Phase of our course. The agent learns from experience and gets better and better.

We do not require any model of the environment for Monte Carlo Methods, which makes them completely different from Dynamic Programming methods.

We solve the “prediction” and “control” problems inherent to any Reinforcement Learning method using Monte Carlo Methods.

Interactive Example to demonstrate on-policy Monte-Carlo control: https://claude.ai/public/artifacts/1e531122-1b45-4337-86a2-df8bc2ac531b

Interactive Example to demonstrate Importance Sampling Ratio used in off-policy Monte-Carlo methods (not the focus of this lecture): https://claude.ai/public/artifacts/8a773548-d360-418b-a20b-d427dc7a1952

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