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Lesson Plan
1
Markov Decision Processes
2
Value Functions & Bellman Equations
3
Policy Gradient Methods
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Explain the Bellman equation in simple terms...
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Lesson Plan: Reinforcement Learning

Markov Decision Processes

States, actions, transitions, rewards

Mastered

Value Functions

V(s), Q(s,a), Bellman equations

Mastered

Policy Gradient Methods

REINFORCE, advantage functions

In progress

Actor-Critic & PPO

Combining value & policy methods

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What is the key difference between on-policy and off-policy learning?

A) Learning rate
B) Data source
C) Network size
D) Reward function

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Bellman Optimality Equation

V*(s) = max_a Σ P(s'|s,a)[R + γV*(s')]

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Key Concepts

MDP provides the mathematical framework

Value functions estimate expected returns

Policy gradients optimize directly

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