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fix: a hundred epochs

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Mahdi Dibaiee 2017-04-04 11:23:44 +04:30
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@ -42,4 +42,4 @@ My interpretation is that after finding a local maximum for accumulated reward a
the updates become pretty large and will pull the model too much to sides, thus the model will enter a state of oscillation.
To try it yourself, there is a `long.npy` file, rename it to `load.npy` (backup `load.npy` before doing so) and run `demo.py`,
you will see the bird failing more often than not. `long.py` was trained for 100 more iterations than `load.npy`.
you will see the bird failing more often than not. `long.py` was trained for only 100 more epochs than `load.npy`.