Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

PPO2: Call total_episode_reward_logger before incrementing num_timesteps #556

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
+6 −6
Open

PPO2: Call total_episode_reward_logger before incrementing num_timesteps #556

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions docs/misc/changelog.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,7 @@ Bug Fixes:
- Fix seeding, so it is now possible to have deterministic results on cpu
- Fix a bug in DDPG where `predict` method with `deterministic=False` would fail
- Fix a bug in TRPO: mean_losses was not initialized causing the logger to crash when there was no gradients (@MarvineGothic)
- Fix a bug in PPO2: total_episode_reward_logger should be called before incrementing num_timesteps

Deprecations:
^^^^^^^^^^^^^
Expand Down
11 changes: 5 additions & 6 deletions stable_baselines/ppo2/ppo2.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -333,6 +333,11 @@ def learn(self, total_timesteps, callback=None, log_interval=1, tb_log_name="PPO
cliprange_vf_now = cliprange_vf(frac)
# true_reward is the reward without discount
obs, returns, masks, actions, values, neglogpacs, states, ep_infos, true_reward = runner.run()
if writer is not None:
self.episode_reward = total_episode_reward_logger(self.episode_reward,
true_reward.reshape((self.n_envs, self.n_steps)),
masks.reshape((self.n_envs, self.n_steps)),
writer, self.num_timesteps)
self.num_timesteps += self.n_batch
ep_info_buf.extend(ep_infos)
mb_loss_vals = []
Expand Down Expand Up @@ -373,12 +378,6 @@ def learn(self, total_timesteps, callback=None, log_interval=1, tb_log_name="PPO
t_now = time.time()
fps = int(self.n_batch / (t_now - t_start))

if writer is not None:
self.episode_reward = total_episode_reward_logger(self.episode_reward,
true_reward.reshape((self.n_envs, self.n_steps)),
masks.reshape((self.n_envs, self.n_steps)),
writer, self.num_timesteps)

if self.verbose >= 1 and (update % log_interval == 0 or update == 1):
explained_var = explained_variance(values, returns)
logger.logkv("serial_timesteps", update * self.n_steps)
Expand Down