Comments (1)
A moving average of the cumulative reward for each episode is logged to tensorboard by default. As collecting the coin (and thus completing the level) provides a reward of 10, the mean percentage of levels solved is just the mean reward multiplied by 10. In addition to being logged to tensorboard, the mean reward is also printed during training (see ppo2.py).
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Related Issues (20)
- Why is calling `init_args_and_threads` multiple times from the same process not recommended? HOT 6
- Library missing HOT 1
- How To Use HOT 14
- Easy Way to Control the Procedural Generation HOT 1
- env can not set seed to produce the same env or bug?
- trouble with fetching the logs HOT 2
- How to check Done/Termination condition in the Scalarised version of Coinrun HOT 1
- why the next_state never changes? HOT 4
- Results HOT 2
- Tensorboard HOT 1
- AttributeError: 'CoinRunVecEnv' object has no attribute 'spec' HOT 4
- Environment Make Error 1 HOT 3
- How to reset the environment? HOT 1
- Not working on Windows 10 HOT 3
- Simulating deterministic resets for a single environment HOT 2
- High variance in mean_score during test HOT 1
- batch norm always has is_training = True HOT 2
- How many distinguished environments do you use in evaluation? HOT 1
- Test level generation HOT 1
- Does not compile on the new Apple Silicon - M1 HOT 1
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