Use LR schedule for beta and epsilon #3940
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andrewcoh
commented
May 8, 2020
| learning_rate = tf.train.polynomial_decay( | ||
| lr, global_step, max_step, 1e-10, power=1.0 | ||
| if schedule == ScheduleType.CONSTANT: | ||
| parameter_rate = tf.Variable(parameter, trainable=False) |
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Proposed change(s)
This PR extends the schedule used to decay the learning rate to beta and epsilon. Currently, beta and epsilon are decayed automatically and this is not configurable. This impacts training scenarios like self-play which require some degree of exploration all through out an experiment as the opponent changes.
Additionally, this PR adds beta and epsilon to tensorboard to track these values.
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