Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2407.17032 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908623482912768 |
|---|---|
| author | Towers, Mark Kwiatkowski, Ariel Terry, Jordan Balis, John U. De Cola, Gianluca Deleu, Tristan Goulão, Manuel Kallinteris, Andreas Krimmel, Markus KG, Arjun Perez-Vicente, Rodrigo Pierré, Andrea Schulhoff, Sander Tai, Jun Jet Tan, Hannah Younis, Omar G. |
| author_facet | Towers, Mark Kwiatkowski, Ariel Terry, Jordan Balis, John U. De Cola, Gianluca Deleu, Tristan Goulão, Manuel Kallinteris, Andreas Krimmel, Markus KG, Arjun Perez-Vicente, Rodrigo Pierré, Andrea Schulhoff, Sander Tai, Jun Jet Tan, Hannah Younis, Omar G. |
| contents | Reinforcement Learning (RL) is a continuously growing field that has the potential to revolutionize many areas of artificial intelligence. However, despite its promise, RL research is often hindered by the lack of standardization in environment and algorithm implementations. This makes it difficult for researchers to compare and build upon each other's work, slowing down progress in the field. Gymnasium is an open-source library that provides a standard API for RL environments, aiming to tackle this issue. Gymnasium's main feature is a set of abstractions that allow for wide interoperability between environments and training algorithms, making it easier for researchers to develop and test RL algorithms. In addition, Gymnasium provides a collection of easy-to-use environments, tools for easily customizing environments, and tools to ensure the reproducibility and robustness of RL research. Through this unified framework, Gymnasium significantly streamlines the process of developing and testing RL algorithms, enabling researchers to focus more on innovation and less on implementation details. By providing a standardized platform for RL research, Gymnasium helps to drive forward the field of reinforcement learning and unlock its full potential. Gymnasium is available online at https://github.com/Farama-Foundation/Gymnasium |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17032 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gymnasium: A Standard Interface for Reinforcement Learning Environments Towers, Mark Kwiatkowski, Ariel Terry, Jordan Balis, John U. De Cola, Gianluca Deleu, Tristan Goulão, Manuel Kallinteris, Andreas Krimmel, Markus KG, Arjun Perez-Vicente, Rodrigo Pierré, Andrea Schulhoff, Sander Tai, Jun Jet Tan, Hannah Younis, Omar G. Machine Learning Digital Libraries Reinforcement Learning (RL) is a continuously growing field that has the potential to revolutionize many areas of artificial intelligence. However, despite its promise, RL research is often hindered by the lack of standardization in environment and algorithm implementations. This makes it difficult for researchers to compare and build upon each other's work, slowing down progress in the field. Gymnasium is an open-source library that provides a standard API for RL environments, aiming to tackle this issue. Gymnasium's main feature is a set of abstractions that allow for wide interoperability between environments and training algorithms, making it easier for researchers to develop and test RL algorithms. In addition, Gymnasium provides a collection of easy-to-use environments, tools for easily customizing environments, and tools to ensure the reproducibility and robustness of RL research. Through this unified framework, Gymnasium significantly streamlines the process of developing and testing RL algorithms, enabling researchers to focus more on innovation and less on implementation details. By providing a standardized platform for RL research, Gymnasium helps to drive forward the field of reinforcement learning and unlock its full potential. Gymnasium is available online at https://github.com/Farama-Foundation/Gymnasium |
| title | Gymnasium: A Standard Interface for Reinforcement Learning Environments |
| topic | Machine Learning Digital Libraries |
| url | https://arxiv.org/abs/2407.17032 |