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[tune] clean up population based training prototype (#1478)
* patch up pbt * Sat Jan 27 01:00:03 PST 2018 * Sat Jan 27 01:04:14 PST 2018 * Sat Jan 27 01:04:21 PST 2018 * Sat Jan 27 01:15:15 PST 2018 * Sat Jan 27 01:15:42 PST 2018 * Sat Jan 27 01:16:14 PST 2018 * Sat Jan 27 01:38:42 PST 2018 * Sat Jan 27 01:39:21 PST 2018 * add pbt * Sat Jan 27 01:41:19 PST 2018 * Sat Jan 27 01:44:21 PST 2018 * Sat Jan 27 01:45:46 PST 2018 * Sat Jan 27 16:54:42 PST 2018 * Sat Jan 27 16:57:53 PST 2018 * clean up test * Sat Jan 27 18:01:15 PST 2018 * Sat Jan 27 18:02:54 PST 2018 * Sat Jan 27 18:11:18 PST 2018 * Sat Jan 27 18:11:55 PST 2018 * Sat Jan 27 18:14:09 PST 2018 * review * try out a ppo example * some tweaks to ppo example * add postprocess hook * Sun Jan 28 15:00:40 PST 2018 * clean up custom explore fn * Sun Jan 28 15:10:21 PST 2018 * Sun Jan 28 15:14:53 PST 2018 * Sun Jan 28 15:17:04 PST 2018 * Sun Jan 28 15:33:13 PST 2018 * Sun Jan 28 15:56:40 PST 2018 * Sun Jan 28 15:57:36 PST 2018 * Sun Jan 28 16:00:35 PST 2018 * Sun Jan 28 16:02:58 PST 2018 * Sun Jan 28 16:29:50 PST 2018 * Sun Jan 28 16:30:36 PST 2018 * Sun Jan 28 16:31:44 PST 2018 * improve tune doc * concepts * update humanoid * Fri Feb 2 18:03:33 PST 2018 * fix example * show error file
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@@ -76,13 +76,13 @@ class PPOEvaluator(Evaluator):
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# Value function predictions before the policy update.
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self.prev_vf_preds = tf.placeholder(tf.float32, shape=(None,))
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assert config["sgd_batchsize"] % len(devices) == 0, \
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"Batch size must be evenly divisible by devices"
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if is_remote:
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self.batch_size = config["rollout_batchsize"]
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self.per_device_batch_size = config["rollout_batchsize"]
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else:
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self.batch_size = config["sgd_batchsize"]
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self.batch_size = int(
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config["sgd_batchsize"] / len(devices)) * len(devices)
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assert self.batch_size % len(devices) == 0
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self.per_device_batch_size = int(self.batch_size / len(devices))
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def build_loss(obs, vtargets, advs, acts, plog, pvf_preds):
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