这是indexloc提供的服务,不要输入任何密码
Skip to content

Hotfix: fix reward_metric & n_episode bug in onpolicy trainer #306

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.

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

Merged
merged 1 commit into from
Mar 8, 2021
Merged
Changes from all commits
Commits
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
31 changes: 16 additions & 15 deletions tianshou/trainer/onpolicy.py
Original file line number Diff line number Diff line change
Expand Up @@ -103,7 +103,7 @@ def onpolicy_trainer(
train_fn(epoch, env_step)
result = train_collector.collect(n_step=step_per_collect,
n_episode=episode_per_collect)
if reward_metric:
if result["n/ep"] > 0 and reward_metric:
result["rews"] = reward_metric(result["rews"])
env_step += int(result["n/st"])
t.update(result["n/st"])
Expand All @@ -117,19 +117,20 @@ def onpolicy_trainer(
"n/ep": str(int(result["n/ep"])),
"n/st": str(int(result["n/st"])),
}
if test_in_train and stop_fn and stop_fn(result["rew"]):
test_result = test_episode(
policy, test_collector, test_fn,
epoch, episode_per_test, logger, env_step)
if stop_fn(test_result["rew"]):
if save_fn:
save_fn(policy)
t.set_postfix(**data)
return gather_info(
start_time, train_collector, test_collector,
test_result["rew"], test_result["rew_std"])
else:
policy.train()
if result["n/ep"] > 0:
if test_in_train and stop_fn and stop_fn(result["rew"]):
test_result = test_episode(
policy, test_collector, test_fn,
epoch, episode_per_test, logger, env_step)
if stop_fn(test_result["rew"]):
if save_fn:
save_fn(policy)
t.set_postfix(**data)
return gather_info(
start_time, train_collector, test_collector,
test_result["rew"], test_result["rew_std"])
else:
policy.train()
losses = policy.update(
0, train_collector.buffer,
batch_size=batch_size, repeat=repeat_per_collect)
Expand All @@ -147,7 +148,7 @@ def onpolicy_trainer(
t.update()
# test
test_result = test_episode(policy, test_collector, test_fn, epoch,
episode_per_test, logger, env_step)
episode_per_test, logger, env_step, reward_metric)
rew, rew_std = test_result["rew"], test_result["rew_std"]
if best_epoch == -1 or best_reward < rew:
best_reward, best_reward_std = rew, rew_std
Expand Down