Clean way to stop training#84
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- Avoid using sys.exit() - Use a boolean instead
keon
reviewed
Nov 6, 2018
| # stop training | ||
| if np.mean(scores[-min(10, len(scores)):]) > 490: | ||
| sys.exit() | ||
| complete = True |
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why don't we just break here? and not use complete flag?
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I forgot removing sys.exit(). The sys library may cause trouble when interrupting some configurations
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-------- Message d'origine --------De : Keon <notifications@github.com> Date : 06/11/2018 20:39 (GMT+01:00) À : rlcode/reinforcement-learning <reinforcement-learning@noreply.github.com> Cc : AlexisBogroff <alexis.bogroff@gmail.com>, Author <author@noreply.github.com> Objet : Re: [rlcode/reinforcement-learning] Clean way to stop training (#84)
@keon commented on this pull request.
In 2-cartpole/1-dqn/cartpole_dqn.py:
@@ -162,7 +164,7 @@ def train_model(self):
# if the mean of scores of last 10 episode is bigger than 490
# stop training
if np.mean(scores[-min(10, len(scores)):]) > 490:
- sys.exit()
+ complete = True
why don't we just break here? and not use complete flag?
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dnddnjs
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May 17, 2026
sys.exit() raises SystemExit which short-circuits env.close() and any outer cleanup, can't be unit-tested, and kills the kernel under Jupyter/IPython. Replace with a `solved` flag (DQN/A2C, nested loops) or a plain break (PPO, single loop) so the function returns normally and the final save runs through the same path as the EPISODES-exhausted case. Credit to @AlexisBogroff in #84 for flagging the pattern; applied to the current PyTorch tree.
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Thanks for flagging this @AlexisBogroff! The original |
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Indeed, sys.exit() unexpectedly kills python, rather than properly stopping the script.