Xingdong Zuo
Xingdong Zuo
Load the entire logging folder including multiple configurations and multiple random runs. All the post-processings are performed on the DataFrame, e.g. smoothing the episode returns. e.g. ID | lr |...
Inspired by Amazon SageMaker and Ray, try to refactor the classes for hyperparameters. e.g. - Categorical - Continuous - Logarithmic: for small scale, e.g. learning rate
- In the script folder: remove the "typing" package, it is not necessary anymore.
- Make online statistics as `nn.Parameter` and registered inside the module. It becomes trackable - Similar style with how the BatchNorm is implemented in PyTorch - Different behavior between train/eval...
As an additional backend to Python Multiprocessing.
Support metadata: `reward_range`, `spec` etc
[These two lines](https://github.com/zuoxingdong/dm2gym/blob/ad4e4edb975ba2ffb766d663ff77b8a1c6c5f3d6/dm2gym/envs/dm_suite_env.py#L11-L12)