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文件名称:Reinforcement Learning
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更新时间:2021-05-16 03:27:26
AI
We rst came to focus on what is now known as reinforcement learning in late 1979.
We were both at the University of Massachusetts, working on one of the earliest
projects to revive the idea that networks of neuronlike adaptive elements might prove
to be a promising approach to articial adaptive intelligence. The project explored
the \heterostatic theory of adaptive systems" developed by A. Harry Klopf. Harry's
work was a rich source of ideas, and we were permitted to explore them critically
and compare them with the long history of prior work in adaptive systems. Our
task became one of teasing the ideas apart and understanding their relationships
and relative importance. This continues today, but in 1979 we came to realize that
perhaps the simplest of the ideas, which had long been taken for granted, had received
surprisingly little attention from a computational perspective. This was simply the
idea of a learning system that wants something, that adapts its behavior in order to
maximize a special signal from its environment. This was the idea of a \hedonistic"
learning system, or, as we would say now, the idea of reinforcement learning.