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add tensorboard instruction to demo.md (#105)
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docs/demo/demo.md

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@@ -313,6 +313,17 @@ rm -rf $OUTPUT_DIR && \
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--gin_bindings=train_eval.warmstart_policy_dir=\"$WARMSTART_OUTPUT_DIR/saved_policy\"
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```
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You may also start a tensorboard to monitor the training process with
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```shell
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tensorboard --logdir=$OUTPUT_DIR
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```
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Mainly check the reward_distribution section for the model performance. It
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includes the average reward and the percentile of the reward distributions
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during training. Positive reward means an improvement against the heuristic,
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and negative reward means a regression.
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### Evaluate trained policy on a corpus (Optional)
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Optionally, if you are interested in seeing how the trained policy (`$OUTPUT_DIR/saved_policy`)

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