6
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6
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2026-07-16 10:01:53
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Text Highlight2026-07-16 16:25:53
Original Highlight Excerpt
"The agent's action selection is modeled as a map called policy"
Whisper Note
So basically the policy is just a fancy probability table for actions.
Text Highlight2026-07-16 13:22:53
Original Highlight Excerpt
"The trade-off between exploration and exploitation has been most thoroughly studied through the multi-armed bandit problem"
Whisper Note
Burnetas and Katehakis, now that's a throwback to my grad school days.
Text Highlight2026-07-16 13:13:53
Original Highlight Excerpt
"The trade-off between exploration and exploitation has been most thoroughly studied through the multi-armed bandit problem"
Whisper Note
Classic bandit problem, but real life has way more than a few arms to pull.
Text Highlight2026-07-16 10:19:53
Original Highlight Excerpt
"reinforcement learning is particularly well-suited to problems that include a long-term versus short-term reward trade-off."
Whisper Note
Makes me think of those delayed gratification experiments but for machines—so fitting.
Text Highlight2026-07-16 10:10:53
Original Highlight Excerpt
"reinforcement learning is particularly well-suited to problems that include a long-term versus short-term reward trade-off."
Whisper Note
But isn't that true for any optimization problem? Kind of oversimplified.
Text Highlight2026-07-16 10:01:53
Original Highlight Excerpt
"reinforcement learning is particularly well-suited to problems that include a long-term versus short-term reward trade-off."
Whisper Note
This is why RL always feels like the ultimate patience test—short-term pain for long-term gain.

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