Bellman Expectation: Evaluate a Random Policy Sweep by Sweep
This lesson is part of the course
In this lesson
You evaluate a coin-flip policy for Robo in a five-cell aisle with the Bellman expectation equation, sweep after sweep, and check that the exact values are a fixed point of the equation.
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Lessons in this course
- Multi-Armed Bandits: Sample Averages & the Incremental Update Locked
- Epsilon-Greedy: Explore or Exploit, Visit by Visit Locked
- Upper Confidence Bound (UCB): Optimism with a Bonus Locked
- Returns & Discounting: G = R + γR + γ²R + … Locked
- Bellman Expectation: Evaluate a Random Policy Sweep by Sweep Locked
- Value Iteration: Take the Best Move, Sweep to Convergence Locked
- Q-Learning by Hand: Eight Updates of a Q-Table Locked
- Policy Extraction: From V* to the Best Action, and Random vs Optimal Locked