lint

training.notes

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+# Currently tests:
+- Hidden layer size of 256 shows progress (loss is based on state.q[2])
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+- setting std to zero makes rewards nans why. I wonder if there NEEDS to be randomization in the enviornment
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+- ctrl cost is whats giving nans? interesting?
+- it is unrelated to randomization of enviornmnet. i think gradient related
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+- first thing to become nans seems to be actor loss and scores. after that, everything becomes nans
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+- fixed entropy epsilon. hope this works now.