Multi-Armed Bandits from scratch: three explore-vs-exploit strategies, and why only two of them stop making mistakes
Article summary
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A row of slot machines, each paying out 1 with some hidden probability and 0 otherwise. You have a fixed number of pulls and want the most total reward. Pull the arm that has looked best so far and you might be stuck on an unlucky loser; pull an under-tried arm and you spend a turn that might have paid off. That is the explore-vs-exploit tension, and I built a demo that runs three classic strategies live on the same bandit and races their regret curves. The punchline: they're astonishingly…
1Key Takeaways
- A row of slot machines, each paying out 1 with some hidden probability and 0 otherwise.
- You have a fixed number of pulls and want the most total reward.
- Pull the arm that has looked best so far and you might be stuck on an unlucky loser; pull an under-tried arm and you spend a turn that might have paid off.
- That is the explore-vs-exploit tension, and I built a demo that runs three classic strategies live on the same bandit and races their regret curves.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that a row of slot machines, each paying out 1 with some hidden probability and 0 otherwise.
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