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ai deep-dive Reading Stanford CS229

Reinforcement Learning: MDPs, Value Iteration, and Continuous States

Chapter 19 uses Bellman equations to turn long-horizon decisions into one-step updates, moving from value iteration in known MDPs to model learning and continuous-state approximation.

#cs229#reinforcement-learning#mdp#value-iteration#fitted-value-iteration
learning guide Berkeley CS188 Spring 2026

CS188 MDPs and Reinforcement Learning: From Value Iteration to Q-Learning

Lectures 9–12 and Project 3 use the same Gridworld to contrast value iteration with a known model, Q-learning from unknown dynamics, and approximate Q-learning that generalizes through features.

#berkeley#cs188#reinforcement-learning#mdp#q-learning
learning guide Reading CMU 10-301 Machine Learning

CMU 10-301 HW8: From MDPs to Reinforcement-Learning Updates

HW8 connects states, actions, rewards, transitions, and value updates while separating environment dynamics, policy, and estimation error.

#cmu#machine-learning#reinforcement-learning#mdp