PFRL implements a comprehensive set of DRL algorithms and techniques drawn from the state-of-the-art research in the field, allowing researchers to quickly compare, combine and experiment with them for fast iteration. In particular, PFRL offers high-quality, thoroughly benchmarked implementations attempting to reproduce nine key DRL algorithms which can be used as a base for research and development. By using PFRL, existing ChainerRL users will be able to retain much of their existing code as they migrate to PyTorch.

PFN will provide baseline implementations using PFRL for the MineRL competition at the 2020 Conference on Neural Information Processing Systems (NeurIPS), which participants can use as a starting point to develop their own novel systems. PFRL also provides examples for using Optuna to enable hyperparameter search for DRL applications.

PFN released PFRL in response to the PyTorch community’s demand for a comprehensive DRL library similar to ChainerRL. First launched in February 2017, ChainerRL has been applied internally at PFN and in the Chainer community outside the company in a variety of research and industrial settings. Going forward, PFN aims to use PFRL to accelerate internal research and development, as well as to serve the broader reinforcement learning community.

PFRL is currently available at: https://github.com/pfnet/pfrl

投稿 Preferred Networks Releases PFRL Deep Reinforcement Learning Library for PyTorch Users は Preferred Networks, Inc. に最初に表示されました。

The pytorch-pfn-extras library includes several popular Chainer functions from user feedback during PFN’s transition from the Chainer deep learning framework to PyTorch.

pytorch-pfn-extras includes the following features:

• Extensions and reporter

Functions frequently used when implementing deep learning training programs, such as collecting metrics during training and visualizing training progress

• Automatic inference of parameter sizes

Easier network definitions by automatically inferring the sizes of linear or convolution layer parameters via input sizes

• Distributed snapshots

Reduce the costs of implementing distributed deep learning with automated backup, loading, and generation management of snapshots

pytorch-pfn-extras is available at: https://github.com/pfnet/pytorch-pfn-extras 

The migration guide from Chainer to PyTorch can also be found at: https://medium.com/pytorch/migration-from-chainer-to-pytorch-8ed92c12c8 

On April 6, Optuna was added to the PyTorch Ecosystem of tools that are officially endorsed by the PyTorch community for use in PyTorch-based machine learning and deep learning research and development.

PFN is discussing merging pytorch-pfn-extras features into the PyTorch base build with the PyTorch development team at Facebook, Inc. In response to strong demand from both internal and external users, PFN also aims to release a PyTorch version of the deep reinforcement learning library, ChainerRL, as open-source software by the end of June 2020.  

PFN aims to continue leveraging its software technology it has accumulated through the development of Chainer to contribute to the development of PyTorch and the open-source community.

The PyTorch team at Facebook commented:

“We appreciate PFN for contributing important Chainer functions, such as gathering metrics and managing distributed snapshots, through pytorch-pfn-extras. With this newly available library, PyTorch developers have the ability to understand their model performances and optimize training costs. We look forward to continued collaboration with PFN to bring more contributions to the community, like ChainerRL capabilities later this summer.”

投稿 Preferred Networks Deepens Collaboration with PyTorch Community は Preferred Networks, Inc. に最初に表示されました。