Learning from Scratch by Thinking Fast and Slow
davidbarber.github.io
davidbarber.github.io
The original paper.
The references in the paper paint a much clearer picture of where exactly the idea behind reinforcement learning with optimal, suboptimal, random oracles comes from. There are also mathematical proofs that these setups work.
I was quite shocked to not see [6, 16] references in any of the recent MCTS papers.
These references prove why the stuff works and show how well it works. But the whole field of imitation learning seems invisible to the deep RL papers. Don't have the faintest idea why.
The algorithm described is the ultimate generalized algorithm. If you have the expert policy the algorithm is learning completely supervised, if expert policy is suboptimal but the score (loss) is fully calculable the learned policy will outperform the reference policy, if expert policy is completely random the algorithm behaves as reinforcement learning.
What the paper at the top adds is the ability to improve the expert policy with the learned one simultaneously in unison and the math covered previously guarantees improvement.
seriously ? i have had almost an opposite reaction to this, more in line with : https://jasoncollins.org/2016/06/29/re-reading-kahnemans-thi... (which was discussed here https://news.ycombinator.com/item?id=12030791)
The thing is, Kahneman is likeable, he has a good reputation and books like these are pure candy for the audience who enjoy that kind of literature (myself included)--but that's also a good warning signal. Could it be too simple, too convenient and too satisfying to be true? How do you know when you fall in love with an idea/theory?
But the question remains: what happens when (like in most cases) you don’t have a funnel plot?
I don’t have a good positive answer. I do have several good negative answers.
Decrease your confidence about most things if you’re not sure that you’ve investigated every piece of evidence.
Do not trust websites which are obviously biased (eg Free Republic, Daily Kos, Dr. Oz) when they tell you they’re going to give you “the state of the evidence” on a certain issue, even if the evidence seems very stately indeed. This goes double for any site that contains a list of “myths and facts about X”, quadruple for any site that uses phrases like “ingroup member uses actual FACTS to DEMOLISH the outgroup’s lies about Y”, and octuple for RationalWiki.
Most important, even if someone gives you what seems like overwhelming evidence in favor of a certain point of view, don’t trust it until you’ve done a simple Google search to see if the opposite side has equally overwhelming evidence."But the question remains: what happens when (like in most cases) you don’t have a funnel plot?
"I don’t have a good positive answer. I do have several good negative answers.
"Decrease your confidence about most things if you’re not sure that you’ve investigated every piece of evidence.
"Do not trust websites which are obviously biased (eg Free Republic, Daily Kos, Dr. Oz) when they tell you they’re going to give you “the state of the evidence” on a certain issue, even if the evidence seems very stately indeed. This goes double for any site that contains a list of “myths and facts about X”, quadruple for any site that uses phrases like “ingroup member uses actual FACTS to DEMOLISH the outgroup’s lies about Y”, and octuple for RationalWiki.
"Most important, even if someone gives you what seems like overwhelming evidence in favor of a certain point of view, don’t trust it until you’ve done a simple Google search to see if the opposite side has equally overwhelming evidence."
We're early in our understanding of human behavior. Many of our ideas are wrong. Everything is hard to test. But though the book has flaws and should be evaluated again (would love to see it rewritten), I still think there is usefulness in the models he suggested. And in behavioral economics in general. I think we're far away from having enough replicated studies to be really "sure" of anything, especially with this crisis, but moving in a reasonable direction.
Cognition and metacognition. The highest form of knowing is knowing why. There is an easy to solution to most of this, ruthless application of the scientific method. Ruthless. Zero Ego. Blank Slate every time.
But when the agent doesn't have access to a simulator or a world where it can play, how could it understand the causal relations, and thus, be able to reason? The most important thing for the agent is access to experimentation, and that's why supervised learning (fixed dataset) is fundamentally limited by comparison to RL (environment based learning).