
Reinforcement Learning: An Introduction - fogus
http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html
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the_real_r2d2
In theory yes you could. RL is based on trial and error. The agent senses the
environment in the form of states (ligh=state1, dark=state2) and it performs
actions according to a policy to receive positive or negative rewards. For
example your goal is to have a room always lighten, the agent will sense the
environment (it is dark) it will turn the lights on and it will receive a
positive reward. Eventually the agent will learn that in order to receive
positive rewards it needs to turn on the light when it is dark and turn it off
when it is light. Actually RL it is more complex than that but that is the
basic idea. If you are interested go to scholar.google.com and search for
papers about reinforcement learning. Important authors are Barto, Sutton,
Watkins (Q-Learning), Littman, Kudenko, Stone, Clauss, etc.

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bioweek
Could I train a robotic hand to catch a ball with this? What would be
involved?

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mahmud
That might require you to throw the ball in every possible combination, and
capture a high resolution time series of its positions, in order to train the
AI. Taking into account all the various parameters.

There will also be some heavy image processing, in your motion detection
engine.

I recommend you start with a computer screen, and train your robot to follow
the motion of a virtual ball on the screen. That way you can control every
part of the experiment without a heavy investment in equipment (or all the
pesky details of the real analog world.)

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chromophore
This has been here before : <http://news.ycombinator.com/item?id=795388>

