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开源软件名称:openai-gym开源软件地址:https://gitee.com/mirrors/openai-gym开源软件介绍:GymGym is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. Since its release, Gym's API has become the field standard for doing this. Gym documentation website is at https://www.gymlibrary.ml/, and you can propose fixes and changes here. InstallationTo install the base Gym library, use This does not include dependencies for all families of environments (there's a massive number, and some can be problematic to install on certain systems). You can install these dependencies for one family like We support Python 3.7, 3.8, 3.9 and 3.10 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it. APIThe Gym API's API models environments as simple Python import gymenv = gym.make("CartPole-v1")observation, info = env.reset(seed=42, return_info=True)for _ in range(1000): action = env.action_space.sample() observation, reward, done, info = env.step(action) if done: observation, info = env.reset(return_info=True)env.close() Notable Related Libraries
Environment VersioningGym keeps strict versioning for reproducibility reasons. All environments end in a suffix like "_v0". When changes are made to environments that might impact learning results, the number is increased by one to prevent potential confusion. CitationA whitepaper from when Gym just came out is available https://arxiv.org/pdf/1606.01540, and can be cited with the following bibtex entry: @misc{1606.01540, Author = {Greg Brockman and Vicki Cheung and Ludwig Pettersson and Jonas Schneider and John Schulman and Jie Tang and Wojciech Zaremba}, Title = {OpenAI Gym}, Year = {2016}, Eprint = {arXiv:1606.01540},} Release NotesThere used to be release notes for all the new Gym versions here. New release notes are being moved to releases page on GitHub, like most other libraries do. Old notes can be viewed here. |
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