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[rllib] Support batch norm layers #3369
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Test FAILed. |
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Ping @richardliaw |
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@@ -151,7 +164,7 @@ def build_compute_actions(self, | |||
builder.add_feed_dict({self._prev_action_input: prev_action_batch}) | |||
if self._prev_reward_input is not None and prev_reward_batch: | |||
builder.add_feed_dict({self._prev_reward_input: prev_reward_batch}) | |||
builder.add_feed_dict({self._is_training: is_training}) | |||
builder.add_feed_dict({self._is_training: False}) |
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where in the code is _is_training
True?
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builder.add_feed_dict({self._is_training: True}) |
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A few questions
Co-Authored-By: ericl <ekhliang@gmail.com>
Test FAILed. |
Test FAILed. |
What do these changes do?
is_training
tensor tobuild_layers_v2
test_batch_norm.py
I don't think this will work for e.g., A3C which applies gradient updates separately, but it should work fine in the other execution modes.
Related issue number
Closes: #2023