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Fixed step used to log SAC summary #1008

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Fixed step used to log SAC summary #1008

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0e79512
Fix step used to log SAC summary
krishpop Sep 19, 2020
b7e0f40
Update changelog.rst
krishpop Sep 19, 2020
8123b35
Remove step in SAC._train_step arglist, fixes issue of redundant wrap…
krishpop Sep 21, 2020
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2 changes: 1 addition & 1 deletion docs/misc/changelog.rst
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Expand Up @@ -65,7 +65,7 @@ Bug Fixes:
- Fixed a bug in CloudPickleWrapper's (used by VecEnvs) ``__setstate___`` where loading was incorrectly using ``pickle.loads`` (@shwang).
- Fixed a bug in ``SAC`` and ``TD3`` where the log timesteps was not correct(@YangRui2015)
- Fixed a bug where the environment was reset twice when using ``evaluate_policy``

- Fixed a bug where ``SAC`` uses wrong step to log to tensorboard after multiple calls to ``SAC.learn(..., reset_num_timesteps=True)``
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Deprecations:
^^^^^^^^^^^^^
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3 changes: 2 additions & 1 deletion stable_baselines/sac/sac.py
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Expand Up @@ -314,6 +314,7 @@ def setup_model(self):
self.summary = tf.summary.merge_all()

def _train_step(self, step, writer, learning_rate):
del step
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if step is not used anymore, why not removing it from the arguments?

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good point! I will do that instead, wasn't sure if it followed a train_step template used by other algorithms as well, but it appears that is not the case

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removed step, and fixed issue of wrapping replay_buffer on each call to learn (now only happens on the first one). Although let me know if you think it is better to check if self.model.replay_buffer is wrapped instead using isinstance(self.model.replay_buffer, HindsightExperienceReplayWrapper)

# Sample a batch from the replay buffer
batch = self.replay_buffer.sample(self.batch_size, env=self._vec_normalize_env)
batch_obs, batch_actions, batch_rewards, batch_next_obs, batch_dones = batch
Expand All @@ -336,7 +337,7 @@ def _train_step(self, step, writer, learning_rate):
if writer is not None:
out = self.sess.run([self.summary] + self.step_ops, feed_dict)
summary = out.pop(0)
writer.add_summary(summary, step)
writer.add_summary(summary, self.num_timesteps)
else:
out = self.sess.run(self.step_ops, feed_dict)

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