Epsilon-Greedy: Explore or Exploit, Visit by Visit
This lesson is part of the course
In this lesson
You run an ε-greedy agent for 12 app visits with given random numbers: decide explore or exploit, pick the offer, collect the reward, add up the cumulative reward and chart it against always showing the best offer.
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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