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。

FANUC and PFN formed an R&D alliance*1 in June 2015, followed by a capital alliance*2 in August of the same year to promote a joint development of AI functions for the manufacturing industry that can efficiently improve the performance and operation rates of FANUC products. The newly developed function utilizes machine-learning (ML) technology to predict and control the variable machining accuracy caused by ROBOCUT’s temperature fluctuations, with 30% more accurate compensation than existing method. The new function is applicable from small to large workpieces.

The AI thermal displacement compensation function will be provided as an optional function to ROBOCUT, and FANUC plans to start accepting orders in November of this year. FANUC will also display the ROBOCUT with this new function at Mechatronics Technology Japan, which will be held in Port Messe Nagoya on Oct. 18-21, 2017.

ROBOCUT α-CiB series

FANUC is also developing a similar function for the ROBODRILL series that utilizes ML and expect to release it in the near future.
FANUC and PFN will continue making gradual but steady progress towards realizing innovative manufacturing fields through AI.

“It is my pleasure to announce our first product based on the machine-learning technology since the tie-up with FANUC. Through this product, we can demonstrate using ML is effective in optimizing control parameters, which is one of the most important issues facing the manufacturing industry. PFN will continue to contribute to the intelligence of machine tools and robots by applying machine learning and deep learning techniques.”
Toru Nishikawa, Chief Executive Officer of PFN

＊1 Announcement for R&D alliance with FANUC Corporation
https://www.preferred.jp/en/news/8731
＊2 Announcement for capital tie-up between FANUC and PFN
http://www.fanuc.co.jp/en/profile/pr/newsrelease/notice20150821.html

Note 1. Wire-cut EDM is a precision and fine shape machining tool that uses discharge phenomenon between the ultrathin wire electrode and the metal workpiece (electric conductor).

投稿 AI (Machine Learning) Improves Wire-cut EDM Accuracy は Preferred Networks, Inc. に最初に表示されました。