Comments (1)
The reward does not need to be immediately known. You could assign a reward to only states where the ball is hitting the bat, and after sufficient training it should have figured out an expected reward for all other states as well.
I think the main difficulty here is that you don't know what your opponent is going to do, so you cannot deterministically compute the next state from the current state. You could make it random, but that's not very realistic. Or have it train against another AI.
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Related Issues (11)
- Dumping results to file
- Document your code, please. HOT 1
- Example doesn't "learn" anything HOT 2
- Empty list of actions panics HOT 3
- Sink States HOT 3
- Train by individual steps HOT 1
- Eucdist example doesn't seem to be correct HOT 2
- Can't represent a state from which there are no actions HOT 2
- How to make 2 networks play against each other? HOT 1
- Saving model to disk HOT 2
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