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","archived":false,"fork":false,"pushed_at":"2024-11-04T14:34:30.000Z","size":28018,"stargazers_count":10720,"open_issues_count":109,"forks_count":1390,"subscribers_count":420,"default_branch":"master","last_synced_at":"2025-05-05T14:19:38.730Z","etag":null,"topics":["ai","google","ml","rl","tensorflow"],"latest_commit_sha":null,"homepage":"https://github.com/google/dopamine","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/google.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":"AUTHORS","dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-07-26T09:58:36.000Z","updated_at":"2025-05-04T20:32:21.000Z","dependencies_parsed_at":"2023-01-20T23:16:12.866Z","dependency_job_id":"8fbca299-3a49-4894-b5ed-766f926f61bd","html_url":"https://github.com/google/dopamine","commit_stats":{"total_commits":340,"total_committers":16,"mean_commits":21.25,"dds":0.5617647058823529,"last_synced_commit":"4552f69af4763053d87ee4ce6d3da59ca3232f3c"},"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google%2Fdopamine","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google%2Fdopamine/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google%2Fdopamine/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google%2Fdopamine/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/google","download_url":"https://codeload.github.com/google/dopamine/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253777439,"owners_count":21962688,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","google","ml","rl","tensorflow"],"created_at":"2024-07-30T21:00:51.409Z","updated_at":"2025-05-12T16:34:07.458Z","avatar_url":"https://github.com/google.png","language":"Jupyter Notebook","funding_links":[],"categories":["Toolbox","Jupyter Notebook","Frameworks and Libraries","Uncategorized","Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL)","Reinforcement Learning","Libraries","inbox","Sensor Processing","Jupyter Notebook (7)","TensorFlow Tools, Libraries, and Frameworks","Tools","Training ground","强化学习","时间序列","Online Resources","Reinforcement Learning Tools","TensorFlow Models","Industry Strength Reinforcement Learning","ai","Machine Learning - Reinforcement","General benchmark frameworks","Technologies","RL Frameworks \u0026 Implementations \u003ca name=\"frameworks\"\u003e\u003c/a\u003e"],"sub_categories":["Libraries","Uncategorized","RL/DRL Algorithm Implementations and Software Frameworks","NLP","Others","Machine Learning","网络服务_其他","Books","Reinforcement Learning"],"readme":"# Dopamine\n[Getting Started](#getting-started) |\n[Docs][docs] |\n[Baseline Results][baselines] |\n[Changelist](https://google.github.io/dopamine/docs/changelist)\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://google.github.io/dopamine/images/dopamine_logo.png\"\u003e\u003cbr\u003e\u003cbr\u003e\n\u003c/div\u003e\n\nDopamine is a research framework for fast prototyping of reinforcement learning\nalgorithms. It aims to fill the need for a small, easily grokked codebase in\nwhich users can freely experiment with wild ideas (speculative research).\n\nOur design principles are:\n\n* _Easy experimentation_: Make it easy for new users to run benchmark\n                          experiments.\n* _Flexible development_: Make it easy for new users to try out research ideas.\n* _Compact and reliable_: Provide implementations for a few, battle-tested\n                          algorithms.\n* _Reproducible_: Facilitate reproducibility in results. In particular, our\n                  setup follows the recommendations given by\n                  [Machado et al. (2018)][machado].\n\nDopamine supports the following agents, implemented with jax:\n\n* DQN ([Mnih et al., 2015][dqn])\n* C51 ([Bellemare et al., 2017][c51])\n* Rainbow ([Hessel et al., 2018][rainbow])\n* IQN ([Dabney et al., 2018][iqn])\n* SAC ([Haarnoja et al., 2018][sac])\n* PPO ([Schulman et al., 2017][ppo])\n\nFor more information on the available agents, see the [docs](https://google.github.io/dopamine/docs).\n\nMany of these agents also have a tensorflow (legacy) implementation, though\nnewly added agents are likely to be jax-only.\n\nThis is not an official Google product.\n\n## Getting Started\n\n\nWe provide docker containers for using Dopamine.\nInstructions can be found [here](https://google.github.io/dopamine/docker/).\n\nAlternatively, Dopamine can be installed from source (preferred) or installed\nwith pip. For either of these methods, continue reading at prerequisites.\n\n### Prerequisites\n\nDopamine supports Atari environments and Mujoco environments. Install the\nenvironments you intend to use before you install Dopamine:\n\n**Atari**\n\n1. These should now come packaged with\n   [ale_py](https://github.com/Farama-Foundation/Arcade-Learning-Environment).\n1. You may need to manually run some steps to properly install `baselines`, see\n   [instructions](https://github.com/openai/baselines).\n\n**Mujoco**\n\n1. Install Mujoco and get a license\n[here](https://github.com/openai/mujoco-py#install-mujoco).\n2. Run `pip install mujoco-py` (we recommend using a\n[virtual environment](virtualenv)).\n\n### Installing from Source\n\n\nThe most common way to use Dopamine is to install it from source and modify\nthe source code directly:\n\n```\ngit clone https://github.com/google/dopamine\n```\n\nAfter cloning, install dependencies:\n\n```\npip install -r dopamine/requirements.txt\n```\n\nDopamine supports tensorflow (legacy) and jax (actively maintained) agents.\nView the [Tensorflow documentation](https://www.tensorflow.org/install) for\nmore information on installing tensorflow.\n\nNote: We recommend using a [virtual environment](virtualenv) when working with Dopamine.\n\n### Installing with Pip\n\nNote: We strongly recommend installing from source for most users.\n\nInstalling with pip is simple, but Dopamine is designed to be modified\ndirectly. We recommend installing from source for writing your own experiments.\n\n```\npip install dopamine-rl\n```\n\n### Running tests\n\nYou can test whether the installation was successful by running the following\nfrom the dopamine root directory.\n\n```\nexport PYTHONPATH=$PYTHONPATH:$PWD\npython -m tests.dopamine.atari_init_test\n```\n\n## Next Steps\n\nView the [docs][docs] for more information on training agents.\n\nWe supply [baselines][baselines] for each Dopamine agent.\n\nWe also provide a set of [Colaboratory notebooks](https://github.com/google/dopamine/tree/master/dopamine/colab)\nwhich demonstrate how to use Dopamine.\n\n## References\n\n[Bellemare et al., *The Arcade Learning Environment: An evaluation platform for\ngeneral agents*. Journal of Artificial Intelligence Research, 2013.][ale]\n\n[Machado et al., *Revisiting the Arcade Learning Environment: Evaluation\nProtocols and Open Problems for General Agents*, Journal of Artificial\nIntelligence Research, 2018.][machado]\n\n[Hessel et al., *Rainbow: Combining Improvements in Deep Reinforcement Learning*.\nProceedings of the AAAI Conference on Artificial Intelligence, 2018.][rainbow]\n\n[Mnih et al., *Human-level Control through Deep Reinforcement Learning*. Nature,\n2015.][dqn]\n\n[Schaul et al., *Prioritized Experience Replay*. Proceedings of the International\nConference on Learning Representations, 2016.][prioritized_replay]\n\n[Haarnoja et al., *Soft Actor-Critic Algorithms and Applications*,\narXiv preprint arXiv:1812.05905, 2018.][sac]\n\n[Schulman et al., *Proximal Policy Optimization Algorithms*.][ppo]\n\n## Giving credit\n\nIf you use Dopamine in your work, we ask that you cite our\n[white paper][dopamine_paper]. Here is an example BibTeX entry:\n\n```\n@article{castro18dopamine,\n  author    = {Pablo Samuel Castro and\n               Subhodeep Moitra and\n               Carles Gelada and\n               Saurabh Kumar and\n               Marc G. Bellemare},\n  title     = {Dopamine: {A} {R}esearch {F}ramework for {D}eep {R}einforcement {L}earning},\n  year      = {2018},\n  url       = {http://arxiv.org/abs/1812.06110},\n  archivePrefix = {arXiv}\n}\n```\n\n\n[docs]: https://google.github.io/dopamine/docs/\n[baselines]: https://google.github.io/dopamine/baselines\n[machado]: https://jair.org/index.php/jair/article/view/11182\n[ale]: https://jair.org/index.php/jair/article/view/10819\n[dqn]: https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf\n[a3c]: http://proceedings.mlr.press/v48/mniha16.html\n[prioritized_replay]: https://arxiv.org/abs/1511.05952\n[c51]: http://proceedings.mlr.press/v70/bellemare17a.html\n[rainbow]: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17204/16680\n[iqn]: https://arxiv.org/abs/1806.06923\n[sac]: https://arxiv.org/abs/1812.05905\n[ppo]: https://arxiv.org/abs/1707.06347\n[dopamine_paper]: https://arxiv.org/abs/1812.06110\n[vitualenv]: https://docs.python.org/3/library/venv.html#creating-virtual-environments\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoogle%2Fdopamine","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgoogle%2Fdopamine","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoogle%2Fdopamine/lists"}