Posterior Sampling for Large Scale Reinforcement Learning — mentioned by gwern

Mon, 02 Jun 2025 19:11:48 GMT

https://gwern.net/doc/science/1986-hamming#great-thoughts-fr... You can think of it as a RL problem, and there are some interesting algorithms which achieve good performance by periodically 'breaking out' of exploitation, but less and less: https://arxiv.org/abs/1711.07979 (I expect that you can come up with an infinite hierarchy of 'wake ups' which converge to a fixed overhead and which aren't terribly far off an optimal schedule of replanning wake-ups, and that something like 'great thoughts Friday' is the first step; then you'd have 'great thoughts first-of-the-month' and 'great thoughts…

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Posterior Sampling for Large Scale Reinforcement Learning · HackerPapers discussions

Posterior Sampling for Large Scale Reinforcement Learning — mentioned by gwern