OpenAI, the nonprofit artificial intelligence research company established last year with backing from several Silicon Valley figures, today announced its first product: a proving ground for algorithms for reinforcement learning, which involves training machines to do things based on trial and error. OpenAI is releasing tools you can run locally to test out algorithms in various "environments" -- including Atari games like Air Raid, Breakout, and Ms. Pacman -- and a Web service for sharing test results. The system automatically scores evaluations and also seeks to have results reviewed and reproduced by other people. "We originally built OpenAI Gym as a tool to accelerate our own RL research. We hope it will be just as useful for the broader community," OpenAI's Greg Brockman and John Schulman wrote in a blog post.
Open AI, a non-profit artificial intelligence research company backed by Elon Musk, launched a toolkit for developing and comparing reinforcement learning algorithms. OpenAI Gym is a suite of environments that include simulated robotic tasks and Atari games as well as a website for people to post their results and share code. OpenAI researcher John Schulman shared some details about his organization, why reinforcement learning is important and how the OpenAI Gym will make it easier for AI researchers to design, iterate and improve their next generation applications.
Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through the features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions.