What's Changed
This release includes several new features and bug fixes and relicenses TorchFort to an Apache 2.0 license. Support for gradient clipping and user control of the built-in MLP flattening behavior has been added, along with extending the array dimensions supported by the Fortran supervised and RL training interfaces. Several inconsistencies and bugs in the RL algorithms have been addressed, as well as proper handling of gradient accumulation. Supported tensor dimensions of the built-in MLP model have been made more strict, and it no longer supports multiple input/output.
Breaking changes
#76 explicitly disables multiple input tensors for the built-in MLP mode, making it no longer suitable for use as the critic network in RL training. Replace it with the new CriticMLP model or your own model in PyTorch/TorchScript.
Deprecations
None.
Notable PRs included in this release
- Fixing predefined RL models, adds new CriticMLP for RL (#77)
- Explicitly disallow multiple input tensors from built-in MLP model. (#76)
- Expand Fortran supervised training interface coverage to more input/label and input/output dimension combinations (#79)
- Update to Apache 2.0 license. (#80)
- Add distributed model/system creation routines using Fortran integer MPI_Comm format. Simplify corresponding Fortran interfaces. (#81)
- Add parameter to control flattening behavior of built-in MLP model. (#78)
- Add build configured torchfort_config.h header to indicate GPU support in build. (#83)
- Fixes to built-in ActorCriticMLP. Adding 1D Fortran array interfaces for RL. (#82)
- Use p_model_ instead of p_model_target_ in predict td3 (#87)
- Fixing device placements in SAC RL training (#86)
- Fixes to action squashing in RL training. (#88)
- Making SAC RL training more consistent with spinningup reference (#90)
- Fixing gradient accumulation in RL algorithms. (#93)
- Add support for gradient clipping. (#94)
Full Changelog: v0.3.1...v0.4.0