Why it matters
Probability DP handles randomized problems. Understanding shapes stochastic problem solving.
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The architecture
State: position + relevant info.
Transitions: probability-weighted next states.
Value: expected outcome.
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How it works end to end
Markov chain: state transitions with probabilities.
Stochastic games: min/max over decisions + probability over random events.
Applications: dice games, Markov reward processes, some ML/RL basics.