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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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3 changes: 2 additions & 1 deletion docs/misc/changelog.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -65,7 +65,8 @@ 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)`` (@krishpop)
- Fixed issue where HER replay buffer wrapper is used multiple times after multiple calls to ``HER.learn`` (@krishpop)

Deprecations:
^^^^^^^^^^^^^
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5 changes: 4 additions & 1 deletion stable_baselines/her/her.py
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Expand Up @@ -70,6 +70,7 @@ def _create_replay_wrapper(self, env):
n_sampled_goal=self.n_sampled_goal,
goal_selection_strategy=self.goal_selection_strategy,
wrapped_env=self.env)
self.wrapped_buffer = False

def set_env(self, env):
assert not isinstance(env, VecEnvWrapper), "HER does not support VecEnvWrapper"
Expand Down Expand Up @@ -108,9 +109,11 @@ def setup_model(self):

def learn(self, total_timesteps, callback=None, log_interval=100, tb_log_name="HER",
reset_num_timesteps=True):
replay_wrapper = self.replay_wrapper if not self.wrapped_buffer else None
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this look like another issue... Please open a separate PR (you can include the minimal code and traceback there to reproduce the error).
Although it is small, it is better to solve one problem at a time ;)

self.wrapped_buffer = True
return self.model.learn(total_timesteps, callback=callback, log_interval=log_interval,
tb_log_name=tb_log_name, reset_num_timesteps=reset_num_timesteps,
replay_wrapper=self.replay_wrapper)
replay_wrapper=replay_wrapper)

def _check_obs(self, observation):
if isinstance(observation, dict):
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6 changes: 3 additions & 3 deletions stable_baselines/sac/sac.py
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Expand Up @@ -313,7 +313,7 @@ def setup_model(self):

self.summary = tf.summary.merge_all()

def _train_step(self, step, writer, learning_rate):
def _train_step(self, writer, learning_rate):
# 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 +336,7 @@ 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)

Expand Down Expand Up @@ -460,7 +460,7 @@ def learn(self, total_timesteps, callback=None,
frac = 1.0 - step / total_timesteps
current_lr = self.learning_rate(frac)
# Update policy and critics (q functions)
mb_infos_vals.append(self._train_step(step, writer, current_lr))
mb_infos_vals.append(self._train_step(writer, current_lr))
# Update target network
if (step + grad_step) % self.target_update_interval == 0:
# Update target network
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