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reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.\u003c/h1\u003e\u003ca id=\"user-content-tf-agents-a-reliable-scalable-and-easy-to-use-tensorflow-library-for-contextual-bandits-and-reinforcement-learning\" class=\"anchor\" aria-label=\"Permalink: TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.\" href=\"#tf-agents-a-reliable-scalable-and-easy-to-use-tensorflow-library-for-contextual-bandits-and-reinforcement-learning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://badge.fury.io/py/tf-agents\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/0089f9fb74ccb268eb0e73b3975081e5cb9418b4898d83f9a44b370ee6737da0/68747470733a2f2f62616467652e667572792e696f2f70792f74662d6167656e74732e737667\" alt=\"PyPI tf-agents\" data-canonical-src=\"https://badge.fury.io/py/tf-agents.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/645ceaf7b62b6cb262386175744ee4b057450036b2b8670b42daf736cb90921b/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f74662d6167656e7473\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/645ceaf7b62b6cb262386175744ee4b057450036b2b8670b42daf736cb90921b/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f74662d6167656e7473\" alt=\"PyPI - Python Version\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/tf-agents\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/tensorflow/agents\"\u003eTF-Agents\u003c/a\u003e makes implementing, deploying,\nand testing new Bandits and RL algorithms easier. It provides well tested and\nmodular components that can be modified and extended. It enables fast code\niteration, with good test integration and benchmarking.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo get started, we recommend checking out one of our Colab tutorials. If you\nneed an intro to RL (or a quick recap),\n\u003ca href=\"/tensorflow/agents/blob/master/docs/tutorials/0_intro_rl.ipynb\"\u003estart here\u003c/a\u003e. Otherwise, check out our\n\u003ca href=\"/tensorflow/agents/blob/master/docs/tutorials/1_dqn_tutorial.ipynb\"\u003eDQN tutorial\u003c/a\u003e to get an agent up and\nrunning in the Cartpole environment. API documentation for the current stable\nrelease is on\n\u003ca href=\"https://www.tensorflow.org/agents/api_docs/python/tf_agents\" rel=\"nofollow\"\u003etensorflow.org\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTF-Agents is under active development and interfaces may change at any time.\nFeedback and comments are welcome.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTable of contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of contents\" href=\"#table-of-contents\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#Agents\"\u003eAgents\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"#Tutorials\"\u003eTutorials\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"#Multi-Armed Bandits\"\u003eMulti-Armed Bandits\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"#Examples\"\u003eExamples\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"#Installation\"\u003eInstallation\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"#Contributing\"\u003eContributing\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"#Releases\"\u003eReleases\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"#Principles\"\u003ePrinciples\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"#Contributors\"\u003eContributors\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"#Citation\"\u003eCitation\u003c/a\u003e\u003cbr\u003e\n\u003ca href=\"#Disclaimer\"\u003eDisclaimer\u003c/a\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca id=\"user-content-agents\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAgents\u003c/h2\u003e\u003ca id=\"user-content-agents\" class=\"anchor\" aria-label=\"Permalink: Agents\" href=\"#agents\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn TF-Agents, the core elements of RL algorithms are implemented as \u003ccode\u003eAgents\u003c/code\u003e. An\nagent encompasses two main responsibilities: defining a Policy to interact with\nthe Environment, and how to learn/train that Policy from collected experience.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCurrently the following algorithms are available under TF-Agents:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://deepmind.com/research/dqn/\" rel=\"nofollow\"\u003eDQN: \u003cstrong\u003eHuman level control through deep reinforcement learning\u003c/strong\u003e Mnih et\nal., 2015\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1509.06461\" rel=\"nofollow\"\u003eDDQN: \u003cstrong\u003eDeep Reinforcement Learning with Double Q-learning\u003c/strong\u003e Hasselt et\nal., 2015\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1509.02971\" rel=\"nofollow\"\u003eDDPG: \u003cstrong\u003eContinuous control with deep reinforcement learning\u003c/strong\u003e Lillicrap et\nal., 2015\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1802.09477\" rel=\"nofollow\"\u003eTD3: \u003cstrong\u003eAddressing Function Approximation Error in Actor-Critic Methods\u003c/strong\u003e\nFujimoto et al., 2018\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf\" rel=\"nofollow\"\u003eREINFORCE: \u003cstrong\u003eSimple Statistical Gradient-Following Algorithms for\nConnectionist Reinforcement Learning\u003c/strong\u003e Williams,\n1992\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1707.06347\" rel=\"nofollow\"\u003ePPO: \u003cstrong\u003eProximal Policy Optimization Algorithms\u003c/strong\u003e Schulman et al., 2017\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1812.05905\" rel=\"nofollow\"\u003eSAC: \u003cstrong\u003eSoft Actor Critic\u003c/strong\u003e Haarnoja et al., 2018\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca id=\"user-content-tutorials\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTutorials\u003c/h2\u003e\u003ca id=\"user-content-tutorials\" class=\"anchor\" aria-label=\"Permalink: Tutorials\" href=\"#tutorials\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSee \u003ca href=\"/tensorflow/agents/blob/master/docs/tutorials\"\u003e\u003ccode\u003edocs/tutorials/\u003c/code\u003e\u003c/a\u003e for tutorials on the major components\nprovided.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca id=\"user-content-multi-armed bandits\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMulti-Armed Bandits\u003c/h2\u003e\u003ca id=\"user-content-multi-armed-bandits\" class=\"anchor\" aria-label=\"Permalink: Multi-Armed Bandits\" href=\"#multi-armed-bandits\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe TF-Agents library contains a comprehensive Multi-Armed Bandits suite,\nincluding Bandits environments and agents. RL agents can also be used on Bandit\nenvironments. There is a tutorial in\n\u003ca href=\"https://github.com/tensorflow/agents/tree/master/docs/tutorials/bandits_tutorial.ipynb\"\u003e\u003ccode\u003ebandits_tutorial.ipynb\u003c/code\u003e\u003c/a\u003e.\nand ready-to-run examples in\n\u003ca href=\"https://github.com/tensorflow/agents/tree/master/tf_agents/bandits/agents/examples/v2\"\u003e\u003ccode\u003etf_agents/bandits/agents/examples/v2\u003c/code\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca id=\"user-content-examples\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eEnd-to-end examples training agents can be found under each agent directory.\ne.g.:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDQN:\n\u003ca href=\"https://github.com/tensorflow/agents/tree/master/tf_agents/agents/dqn/examples/v2/train_eval.py\"\u003e\u003ccode\u003etf_agents/agents/dqn/examples/v2/train_eval.py\u003c/code\u003e\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca id=\"user-content-installation\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTF-Agents publishes nightly and stable builds. For a list of releases read the\n\u003ca href=\"#Releases\"\u003eReleases\u003c/a\u003e section. The commands below cover installing\nTF-Agents stable and nightly from \u003ca href=\"https://pypi.org\" rel=\"nofollow\"\u003epypi.org\u003c/a\u003e as well as from a\nGitHub clone.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e⚠️\u003c/g-emoji\u003e If using Reverb (replay buffer), which is very common,\nTF-Agents will only work with Linux.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eNote: Python 3.11 requires pygame 2.1.3+.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStable\u003c/h3\u003e\u003ca id=\"user-content-stable\" class=\"anchor\" aria-label=\"Permalink: Stable\" href=\"#stable\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eRun the commands below to install the most recent stable release. API\ndocumentation for the release is on\n\u003ca href=\"https://www.tensorflow.org/agents/api_docs/python/tf_agents\" rel=\"nofollow\"\u003etensorflow.org\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ pip install --user tf-agents[reverb]\n\n# Use keras-2\n$ export TF_USE_LEGACY_KERAS=1\n# Use this tag get the matching examples and colabs.\n$ git clone https://github.com/tensorflow/agents.git\n$ cd agents\n$ git checkout v0.18.0\"\u003e\u003cpre\u003e$ pip install --user tf-agents[reverb]\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use keras-2\u003c/span\u003e\n$ \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e TF_USE_LEGACY_KERAS=1\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use this tag get the matching examples and colabs.\u003c/span\u003e\n$ git clone https://github.com/tensorflow/agents.git\n$ \u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e agents\n$ git checkout v0.18.0\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you want to install TF-Agents with versions of Tensorflow or\n\u003ca href=\"https://github.com/deepmind/reverb\"\u003eReverb\u003c/a\u003e that are flagged as not compatible\nby the pip dependency check, use the following pattern below at your own risk.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ pip install --user tensorflow\n$ pip install --user tf-keras\n$ pip install --user dm-reverb\n$ pip install --user tf-agents\"\u003e\u003cpre\u003e$ pip install --user tensorflow\n$ pip install --user tf-keras\n$ pip install --user dm-reverb\n$ pip install --user tf-agents\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you want to use TF-Agents with TensorFlow 1.15 or 2.0, install version 0.3.0:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Newer versions of tensorflow-probability require newer versions of TensorFlow.\n$ pip install tensorflow-probability==0.8.0\n$ pip install tf-agents==0.3.0\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Newer versions of tensorflow-probability require newer versions of TensorFlow.\u003c/span\u003e\n$ pip install tensorflow-probability==0.8.0\n$ pip install tf-agents==0.3.0\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNightly\u003c/h3\u003e\u003ca id=\"user-content-nightly\" class=\"anchor\" aria-label=\"Permalink: Nightly\" href=\"#nightly\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNightly builds include newer features, but may be less stable than the versioned\nreleases. The nightly build is pushed as \u003ccode\u003etf-agents-nightly\u003c/code\u003e. We suggest\ninstalling nightly versions of TensorFlow (\u003ccode\u003etf-nightly\u003c/code\u003e) and TensorFlow\nProbability (\u003ccode\u003etfp-nightly\u003c/code\u003e) as those are the versions TF-Agents nightly are\ntested against.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo install the nightly build version, run the following:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Use keras-2\n$ export TF_USE_LEGACY_KERAS=1\n\n# `--force-reinstall helps guarantee the right versions.\n$ pip install --user --force-reinstall tf-nightly\n$ pip install --user --force-reinstall tf-keras-nightly\n$ pip install --user --force-reinstall tfp-nightly\n$ pip install --user --force-reinstall dm-reverb-nightly\n\n# Installing with the `--upgrade` flag ensures you'll get the latest version.\n$ pip install --user --upgrade tf-agents-nightly\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use keras-2\u003c/span\u003e\n$ \u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e TF_USE_LEGACY_KERAS=1\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e `--force-reinstall helps guarantee the right versions.\u003c/span\u003e\n$ pip install --user --force-reinstall tf-nightly\n$ pip install --user --force-reinstall tf-keras-nightly\n$ pip install --user --force-reinstall tfp-nightly\n$ pip install --user --force-reinstall dm-reverb-nightly\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Installing with the `--upgrade` flag ensures you'll get the latest version.\u003c/span\u003e\n$ pip install --user --upgrade tf-agents-nightly\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFrom GitHub\u003c/h3\u003e\u003ca id=\"user-content-from-github\" class=\"anchor\" aria-label=\"Permalink: From GitHub\" href=\"#from-github\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAfter cloning the repository, the dependencies can be installed by running \u003ccode\u003epip install -e .[tests]\u003c/code\u003e. TensorFlow needs to be installed independently: \u003ccode\u003epip install --user tf-nightly\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca id=\"user-content-contributing\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributing\u003c/h2\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe're eager to collaborate with you! See \u003ca href=\"/tensorflow/agents/blob/master/CONTRIBUTING.md\"\u003e\u003ccode\u003eCONTRIBUTING.md\u003c/code\u003e\u003c/a\u003e\nfor a guide on how to contribute. This project adheres to TensorFlow's\n\u003ca href=\"/tensorflow/agents/blob/master/CODE_OF_CONDUCT.md\"\u003ecode of conduct\u003c/a\u003e. By participating, you are expected to\nuphold this code.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca id=\"user-content-releases\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReleases\u003c/h2\u003e\u003ca id=\"user-content-releases\" class=\"anchor\" aria-label=\"Permalink: Releases\" href=\"#releases\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTF Agents has stable and nightly releases. The nightly releases are often fine\nbut can have issues due to upstream libraries being in flux. The table below\nlists the version(s) of TensorFlow that align with each TF Agents' release.\nRelease versions of interest:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e0.19.0 supports tensorflow-2.15.0.\u003c/li\u003e\n\u003cli\u003e0.18.0 dropped Python 3.8 support.\u003c/li\u003e\n\u003cli\u003e0.16.0 is the first version to support Python 3.11.\u003c/li\u003e\n\u003cli\u003e0.15.0 is the last release compatible with Python 3.7.\u003c/li\u003e\n\u003cli\u003eIf using numpy \u0026lt; 1.19, then use TF-Agents 0.15.0 or earlier.\u003c/li\u003e\n\u003cli\u003e0.9.0 is the last release compatible with Python 3.6.\u003c/li\u003e\n\u003cli\u003e0.3.0 is the last release compatible with Python 2.x.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eRelease\u003c/th\u003e\n\u003cth\u003eBranch / Tag\u003c/th\u003e\n\u003cth\u003eTensorFlow Version\u003c/th\u003e\n\u003cth\u003edm-reverb Version\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNightly\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents\"\u003emaster\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003etf-nightly\u003c/td\u003e\n\u003ctd\u003edm-reverb-nightly\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.19.0\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents/tree/v0.19.0\"\u003ev0.19.0\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.15.0\u003c/td\u003e\n\u003ctd\u003e0.14.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.18.0\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents/tree/v0.18.0\"\u003ev0.18.0\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.14.0\u003c/td\u003e\n\u003ctd\u003e0.13.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.17.0\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents/tree/v0.17.0\"\u003ev0.17.0\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.13.0\u003c/td\u003e\n\u003ctd\u003e0.12.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.16.0\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents/tree/v0.16.0\"\u003ev0.16.0\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.12.0\u003c/td\u003e\n\u003ctd\u003e0.11.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.15.0\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents/tree/v0.15.0\"\u003ev0.15.0\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.11.0\u003c/td\u003e\n\u003ctd\u003e0.10.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.14.0\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents/tree/v0.14.0\"\u003ev0.14.0\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.10.0\u003c/td\u003e\n\u003ctd\u003e0.9.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.13.0\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents/tree/v0.13.0\"\u003ev0.13.0\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.9.0\u003c/td\u003e\n\u003ctd\u003e0.8.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.12.0\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents/tree/v0.12.0\"\u003ev0.12.0\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.8.0\u003c/td\u003e\n\u003ctd\u003e0.7.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.11.0\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents/tree/v0.11.0\"\u003ev0.11.0\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.7.0\u003c/td\u003e\n\u003ctd\u003e0.6.0\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.10.0\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents/tree/v0.10.0\"\u003ev0.10.0\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.6.0\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.9.0\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents/tree/v0.9.0\"\u003ev0.9.0\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.6.0\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.8.0\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents/tree/v0.8.0\"\u003ev0.8.0\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.5.0\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.7.1\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents/tree/v0.7.1\"\u003ev0.7.1\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.4.0\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.6.0\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/tensorflow/agents/tree/v0.6.0\"\u003ev0.6.0\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e2.3.0\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e0.5.0\u003c/td\u003e\n\u003ctd\u003e\u003ca 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dir=\"auto\"\u003ePrinciples\u003c/h2\u003e\u003ca id=\"user-content-principles\" class=\"anchor\" aria-label=\"Permalink: Principles\" href=\"#principles\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis project adheres to \u003ca href=\"/tensorflow/agents/blob/master/PRINCIPLES.md\"\u003eGoogle's AI principles\u003c/a\u003e. By\nparticipating, using or contributing to this project you are expected to adhere\nto these principles.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca id=\"user-content-contributors\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributors\u003c/h2\u003e\u003ca id=\"user-content-contributors\" class=\"anchor\" aria-label=\"Permalink: Contributors\" href=\"#contributors\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe would like to recognize the following individuals for their code\ncontributions, discussions, and other work to make the TF-Agents library.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eJames Davidson\u003c/li\u003e\n\u003cli\u003eEthan Holly\u003c/li\u003e\n\u003cli\u003eToby Boyd\u003c/li\u003e\n\u003cli\u003eSummer Yue\u003c/li\u003e\n\u003cli\u003eRobert Ormandi\u003c/li\u003e\n\u003cli\u003eKuang-Huei Lee\u003c/li\u003e\n\u003cli\u003eAlexa Greenberg\u003c/li\u003e\n\u003cli\u003eAmir Yazdanbakhsh\u003c/li\u003e\n\u003cli\u003eYao Lu\u003c/li\u003e\n\u003cli\u003eGaurav Jain\u003c/li\u003e\n\u003cli\u003eChristof Angermueller\u003c/li\u003e\n\u003cli\u003eMark Daoust\u003c/li\u003e\n\u003cli\u003eAdam Wood\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca id=\"user-content-citation\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you use this code, please cite it as:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@misc{TFAgents,\n title = {{TF-Agents}: A library for Reinforcement Learning in TensorFlow},\n author = {Sergio Guadarrama and Anoop Korattikara and Oscar Ramirez and\n Pablo Castro and Ethan Holly and Sam Fishman and Ke Wang and\n Ekaterina Gonina and Neal Wu and Efi Kokiopoulou and Luciano Sbaiz and\n Jamie Smith and Gábor Bartók and Jesse Berent and Chris Harris and\n Vincent Vanhoucke and Eugene Brevdo},\n howpublished = {\\url{https://github.com/tensorflow/agents}},\n url = \u0026quot;https://github.com/tensorflow/agents\u0026quot;,\n year = 2018,\n note = \u0026quot;[Online; accessed 25-June-2019]\u0026quot;\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@misc{TFAgents,\n title = {{TF-Agents}: A library for Reinforcement Learning in TensorFlow},\n author = {Sergio Guadarrama and Anoop Korattikara and Oscar Ramirez and\n Pablo Castro and Ethan Holly and Sam Fishman and Ke Wang and\n Ekaterina Gonina and Neal Wu and Efi Kokiopoulou and Luciano Sbaiz and\n Jamie Smith and Gábor Bartók and Jesse Berent and Chris Harris and\n Vincent Vanhoucke and Eugene Brevdo},\n howpublished = {\\url{https://github.com/tensorflow/agents}},\n url = \"https://github.com/tensorflow/agents\",\n year = 2018,\n note = \"[Online; accessed 25-June-2019]\"\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca id=\"user-content-disclaimer\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDisclaimer\u003c/h2\u003e\u003ca id=\"user-content-disclaimer\" class=\"anchor\" aria-label=\"Permalink: Disclaimer\" href=\"#disclaimer\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis is not an official Google product.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.","anchor":"tf-agents-a-reliable-scalable-and-easy-to-use-tensorflow-library-for-contextual-bandits-and-reinforcement-learning","htmlText":"TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning."},{"level":2,"text":"Table of contents","anchor":"table-of-contents","htmlText":"Table of contents"},{"level":2,"text":"Agents","anchor":"agents","htmlText":"Agents"},{"level":2,"text":"Tutorials","anchor":"tutorials","htmlText":"Tutorials"},{"level":2,"text":"Multi-Armed Bandits","anchor":"multi-armed-bandits","htmlText":"Multi-Armed Bandits"},{"level":2,"text":"Examples","anchor":"examples","htmlText":"Examples"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":3,"text":"Stable","anchor":"stable","htmlText":"Stable"},{"level":3,"text":"Nightly","anchor":"nightly","htmlText":"Nightly"},{"level":3,"text":"From GitHub","anchor":"from-github","htmlText":"From 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5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.</h1><a id="user-content-tf-agents-a-reliable-scalable-and-easy-to-use-tensorflow-library-for-contextual-bandits-and-reinforcement-learning" class="anchor" aria-label="Permalink: TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning." href="#tf-agents-a-reliable-scalable-and-easy-to-use-tensorflow-library-for-contextual-bandits-and-reinforcement-learning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://badge.fury.io/py/tf-agents" rel="nofollow"><img src="https://camo.githubusercontent.com/0089f9fb74ccb268eb0e73b3975081e5cb9418b4898d83f9a44b370ee6737da0/68747470733a2f2f62616467652e667572792e696f2f70792f74662d6167656e74732e737667" alt="PyPI tf-agents" data-canonical-src="https://badge.fury.io/py/tf-agents.svg" style="max-width: 100%;"></a> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/645ceaf7b62b6cb262386175744ee4b057450036b2b8670b42daf736cb90921b/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f74662d6167656e7473"><img src="https://camo.githubusercontent.com/645ceaf7b62b6cb262386175744ee4b057450036b2b8670b42daf736cb90921b/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f74662d6167656e7473" alt="PyPI - Python Version" data-canonical-src="https://img.shields.io/pypi/pyversions/tf-agents" style="max-width: 100%;"></a></p> <p dir="auto"><a href="https://github.com/tensorflow/agents">TF-Agents</a> makes implementing, deploying, and testing new Bandits and RL algorithms easier. It provides well tested and modular components that can be modified and extended. It enables fast code iteration, with good test integration and benchmarking.</p> <p dir="auto">To get started, we recommend checking out one of our Colab tutorials. If you need an intro to RL (or a quick recap), <a href="/tensorflow/agents/blob/master/docs/tutorials/0_intro_rl.ipynb">start here</a>. Otherwise, check out our <a href="/tensorflow/agents/blob/master/docs/tutorials/1_dqn_tutorial.ipynb">DQN tutorial</a> to get an agent up and running in the Cartpole environment. API documentation for the current stable release is on <a href="https://www.tensorflow.org/agents/api_docs/python/tf_agents" rel="nofollow">tensorflow.org</a>.</p> <p dir="auto">TF-Agents is under active development and interfaces may change at any time. Feedback and comments are welcome.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Table of contents</h2><a id="user-content-table-of-contents" class="anchor" aria-label="Permalink: Table of contents" href="#table-of-contents"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="#Agents">Agents</a><br> <a href="#Tutorials">Tutorials</a><br> <a href="#Multi-Armed Bandits">Multi-Armed Bandits</a><br> <a href="#Examples">Examples</a><br> <a href="#Installation">Installation</a><br> <a href="#Contributing">Contributing</a><br> <a href="#Releases">Releases</a><br> <a href="#Principles">Principles</a><br> <a href="#Contributors">Contributors</a><br> <a href="#Citation">Citation</a><br> <a href="#Disclaimer">Disclaimer</a><br></p> <p dir="auto"><a id="user-content-agents"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Agents</h2><a id="user-content-agents" class="anchor" aria-label="Permalink: Agents" href="#agents"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">In TF-Agents, the core elements of RL algorithms are implemented as <code>Agents</code>. An agent encompasses two main responsibilities: defining a Policy to interact with the Environment, and how to learn/train that Policy from collected experience.</p> <p dir="auto">Currently the following algorithms are available under TF-Agents:</p> <ul dir="auto"> <li><a href="https://deepmind.com/research/dqn/" rel="nofollow">DQN: <strong>Human level control through deep reinforcement learning</strong> Mnih et al., 2015</a></li> <li><a href="https://arxiv.org/abs/1509.06461" rel="nofollow">DDQN: <strong>Deep Reinforcement Learning with Double Q-learning</strong> Hasselt et al., 2015</a></li> <li><a href="https://arxiv.org/abs/1509.02971" rel="nofollow">DDPG: <strong>Continuous control with deep reinforcement learning</strong> Lillicrap et al., 2015</a></li> <li><a href="https://arxiv.org/abs/1802.09477" rel="nofollow">TD3: <strong>Addressing Function Approximation Error in Actor-Critic Methods</strong> Fujimoto et al., 2018</a></li> <li><a href="https://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf" rel="nofollow">REINFORCE: <strong>Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning</strong> Williams, 1992</a></li> <li><a href="https://arxiv.org/abs/1707.06347" rel="nofollow">PPO: <strong>Proximal Policy Optimization Algorithms</strong> Schulman et al., 2017</a></li> <li><a href="https://arxiv.org/abs/1812.05905" rel="nofollow">SAC: <strong>Soft Actor Critic</strong> Haarnoja et al., 2018</a></li> </ul> <p dir="auto"><a id="user-content-tutorials"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Tutorials</h2><a id="user-content-tutorials" class="anchor" aria-label="Permalink: Tutorials" href="#tutorials"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">See <a href="/tensorflow/agents/blob/master/docs/tutorials"><code>docs/tutorials/</code></a> for tutorials on the major components provided.</p> <p dir="auto"><a id="user-content-multi-armed bandits"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Multi-Armed Bandits</h2><a id="user-content-multi-armed-bandits" class="anchor" aria-label="Permalink: Multi-Armed Bandits" href="#multi-armed-bandits"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The TF-Agents library contains a comprehensive Multi-Armed Bandits suite, including Bandits environments and agents. RL agents can also be used on Bandit environments. There is a tutorial in <a href="https://github.com/tensorflow/agents/tree/master/docs/tutorials/bandits_tutorial.ipynb"><code>bandits_tutorial.ipynb</code></a>. and ready-to-run examples in <a href="https://github.com/tensorflow/agents/tree/master/tf_agents/bandits/agents/examples/v2"><code>tf_agents/bandits/agents/examples/v2</code></a>.</p> <p dir="auto"><a id="user-content-examples"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Examples</h2><a id="user-content-examples" class="anchor" aria-label="Permalink: Examples" href="#examples"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">End-to-end examples training agents can be found under each agent directory. e.g.:</p> <ul dir="auto"> <li>DQN: <a href="https://github.com/tensorflow/agents/tree/master/tf_agents/agents/dqn/examples/v2/train_eval.py"><code>tf_agents/agents/dqn/examples/v2/train_eval.py</code></a></li> </ul> <p dir="auto"><a id="user-content-installation"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">TF-Agents publishes nightly and stable builds. For a list of releases read the <a href="#Releases">Releases</a> section. The commands below cover installing TF-Agents stable and nightly from <a href="https://pypi.org" rel="nofollow">pypi.org</a> as well as from a GitHub clone.</p> <blockquote> <p dir="auto"><g-emoji class="g-emoji" alias="warning">⚠️</g-emoji> If using Reverb (replay buffer), which is very common, TF-Agents will only work with Linux.</p> </blockquote> <blockquote> <p dir="auto">Note: Python 3.11 requires pygame 2.1.3+.</p> </blockquote> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Stable</h3><a id="user-content-stable" class="anchor" aria-label="Permalink: Stable" href="#stable"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Run the commands below to install the most recent stable release. API documentation for the release is on <a href="https://www.tensorflow.org/agents/api_docs/python/tf_agents" rel="nofollow">tensorflow.org</a>.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ pip install --user tf-agents[reverb] # Use keras-2 $ export TF_USE_LEGACY_KERAS=1 # Use this tag get the matching examples and colabs. $ git clone https://github.com/tensorflow/agents.git $ cd agents $ git checkout v0.18.0"><pre>$ pip install --user tf-agents[reverb] <span class="pl-c"><span class="pl-c">#</span> Use keras-2</span> $ <span class="pl-k">export</span> TF_USE_LEGACY_KERAS=1 <span class="pl-c"><span class="pl-c">#</span> Use this tag get the matching examples and colabs.</span> $ git clone https://github.com/tensorflow/agents.git $ <span class="pl-c1">cd</span> agents $ git checkout v0.18.0</pre></div> <p dir="auto">If you want to install TF-Agents with versions of Tensorflow or <a href="https://github.com/deepmind/reverb">Reverb</a> that are flagged as not compatible by the pip dependency check, use the following pattern below at your own risk.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ pip install --user tensorflow $ pip install --user tf-keras $ pip install --user dm-reverb $ pip install --user tf-agents"><pre>$ pip install --user tensorflow $ pip install --user tf-keras $ pip install --user dm-reverb $ pip install --user tf-agents</pre></div> <p dir="auto">If you want to use TF-Agents with TensorFlow 1.15 or 2.0, install version 0.3.0:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Newer versions of tensorflow-probability require newer versions of TensorFlow. $ pip install tensorflow-probability==0.8.0 $ pip install tf-agents==0.3.0"><pre><span class="pl-c"><span class="pl-c">#</span> Newer versions of tensorflow-probability require newer versions of TensorFlow.</span> $ pip install tensorflow-probability==0.8.0 $ pip install tf-agents==0.3.0</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Nightly</h3><a id="user-content-nightly" class="anchor" aria-label="Permalink: Nightly" href="#nightly"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Nightly builds include newer features, but may be less stable than the versioned releases. The nightly build is pushed as <code>tf-agents-nightly</code>. We suggest installing nightly versions of TensorFlow (<code>tf-nightly</code>) and TensorFlow Probability (<code>tfp-nightly</code>) as those are the versions TF-Agents nightly are tested against.</p> <p dir="auto">To install the nightly build version, run the following:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Use keras-2 $ export TF_USE_LEGACY_KERAS=1 # `--force-reinstall helps guarantee the right versions. $ pip install --user --force-reinstall tf-nightly $ pip install --user --force-reinstall tf-keras-nightly $ pip install --user --force-reinstall tfp-nightly $ pip install --user --force-reinstall dm-reverb-nightly # Installing with the `--upgrade` flag ensures you'll get the latest version. $ pip install --user --upgrade tf-agents-nightly"><pre><span class="pl-c"><span class="pl-c">#</span> Use keras-2</span> $ <span class="pl-k">export</span> TF_USE_LEGACY_KERAS=1 <span class="pl-c"><span class="pl-c">#</span> `--force-reinstall helps guarantee the right versions.</span> $ pip install --user --force-reinstall tf-nightly $ pip install --user --force-reinstall tf-keras-nightly $ pip install --user --force-reinstall tfp-nightly $ pip install --user --force-reinstall dm-reverb-nightly <span class="pl-c"><span class="pl-c">#</span> Installing with the `--upgrade` flag ensures you'll get the latest version.</span> $ pip install --user --upgrade tf-agents-nightly</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">From GitHub</h3><a id="user-content-from-github" class="anchor" aria-label="Permalink: From GitHub" href="#from-github"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">After cloning the repository, the dependencies can be installed by running <code>pip install -e .[tests]</code>. TensorFlow needs to be installed independently: <code>pip install --user tf-nightly</code>.</p> <p dir="auto"><a id="user-content-contributing"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contributing</h2><a id="user-content-contributing" class="anchor" aria-label="Permalink: Contributing" href="#contributing"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We're eager to collaborate with you! See <a href="/tensorflow/agents/blob/master/CONTRIBUTING.md"><code>CONTRIBUTING.md</code></a> for a guide on how to contribute. This project adheres to TensorFlow's <a href="/tensorflow/agents/blob/master/CODE_OF_CONDUCT.md">code of conduct</a>. By participating, you are expected to uphold this code.</p> <p dir="auto"><a id="user-content-releases"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Releases</h2><a id="user-content-releases" class="anchor" aria-label="Permalink: Releases" href="#releases"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">TF Agents has stable and nightly releases. The nightly releases are often fine but can have issues due to upstream libraries being in flux. The table below lists the version(s) of TensorFlow that align with each TF Agents' release. Release versions of interest:</p> <ul dir="auto"> <li>0.19.0 supports tensorflow-2.15.0.</li> <li>0.18.0 dropped Python 3.8 support.</li> <li>0.16.0 is the first version to support Python 3.11.</li> <li>0.15.0 is the last release compatible with Python 3.7.</li> <li>If using numpy < 1.19, then use TF-Agents 0.15.0 or earlier.</li> <li>0.9.0 is the last release compatible with Python 3.6.</li> <li>0.3.0 is the last release compatible with Python 2.x.</li> </ul> <markdown-accessiblity-table><table> <thead> <tr> <th>Release</th> <th>Branch / Tag</th> <th>TensorFlow Version</th> <th>dm-reverb Version</th> </tr> </thead> <tbody> <tr> <td>Nightly</td> <td><a href="https://github.com/tensorflow/agents">master</a></td> <td>tf-nightly</td> <td>dm-reverb-nightly</td> </tr> <tr> <td>0.19.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.19.0">v0.19.0</a></td> <td>2.15.0</td> <td>0.14.0</td> </tr> <tr> <td>0.18.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.18.0">v0.18.0</a></td> <td>2.14.0</td> <td>0.13.0</td> </tr> <tr> <td>0.17.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.17.0">v0.17.0</a></td> <td>2.13.0</td> <td>0.12.0</td> </tr> <tr> <td>0.16.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.16.0">v0.16.0</a></td> <td>2.12.0</td> <td>0.11.0</td> </tr> <tr> <td>0.15.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.15.0">v0.15.0</a></td> <td>2.11.0</td> <td>0.10.0</td> </tr> <tr> <td>0.14.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.14.0">v0.14.0</a></td> <td>2.10.0</td> <td>0.9.0</td> </tr> <tr> <td>0.13.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.13.0">v0.13.0</a></td> <td>2.9.0</td> <td>0.8.0</td> </tr> <tr> <td>0.12.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.12.0">v0.12.0</a></td> <td>2.8.0</td> <td>0.7.0</td> </tr> <tr> <td>0.11.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.11.0">v0.11.0</a></td> <td>2.7.0</td> <td>0.6.0</td> </tr> <tr> <td>0.10.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.10.0">v0.10.0</a></td> <td>2.6.0</td> <td></td> </tr> <tr> <td>0.9.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.9.0">v0.9.0</a></td> <td>2.6.0</td> <td></td> </tr> <tr> <td>0.8.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.8.0">v0.8.0</a></td> <td>2.5.0</td> <td></td> </tr> <tr> <td>0.7.1</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.7.1">v0.7.1</a></td> <td>2.4.0</td> <td></td> </tr> <tr> <td>0.6.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.6.0">v0.6.0</a></td> <td>2.3.0</td> <td></td> </tr> <tr> <td>0.5.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.5.0">v0.5.0</a></td> <td>2.2.0</td> <td></td> </tr> <tr> <td>0.4.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.4.0">v0.4.0</a></td> <td>2.1.0</td> <td></td> </tr> <tr> <td>0.3.0</td> <td><a href="https://github.com/tensorflow/agents/tree/v0.3.0">v0.3.0</a></td> <td>1.15.0 and 2.0.0.</td> <td></td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto"><a id="user-content-principles"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Principles</h2><a id="user-content-principles" class="anchor" aria-label="Permalink: Principles" href="#principles"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This project adheres to <a href="/tensorflow/agents/blob/master/PRINCIPLES.md">Google's AI principles</a>. By participating, using or contributing to this project you are expected to adhere to these principles.</p> <p dir="auto"><a id="user-content-contributors"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contributors</h2><a id="user-content-contributors" class="anchor" aria-label="Permalink: Contributors" href="#contributors"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We would like to recognize the following individuals for their code contributions, discussions, and other work to make the TF-Agents library.</p> <ul dir="auto"> <li>James Davidson</li> <li>Ethan Holly</li> <li>Toby Boyd</li> <li>Summer Yue</li> <li>Robert Ormandi</li> <li>Kuang-Huei Lee</li> <li>Alexa Greenberg</li> <li>Amir Yazdanbakhsh</li> <li>Yao Lu</li> <li>Gaurav Jain</li> <li>Christof Angermueller</li> <li>Mark Daoust</li> <li>Adam Wood</li> </ul> <p dir="auto"><a id="user-content-citation"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you use this code, please cite it as:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@misc{TFAgents, title = {{TF-Agents}: A library for Reinforcement Learning in TensorFlow}, author = {Sergio Guadarrama and Anoop Korattikara and Oscar Ramirez and Pablo Castro and Ethan Holly and Sam Fishman and Ke Wang and Ekaterina Gonina and Neal Wu and Efi Kokiopoulou and Luciano Sbaiz and Jamie Smith and Gábor Bartók and Jesse Berent and Chris Harris and Vincent Vanhoucke and Eugene Brevdo}, howpublished = {\url{https://github.com/tensorflow/agents}}, url = "https://github.com/tensorflow/agents", year = 2018, note = "[Online; accessed 25-June-2019]" }"><pre class="notranslate"><code>@misc{TFAgents, title = {{TF-Agents}: A library for Reinforcement Learning in TensorFlow}, author = {Sergio Guadarrama and Anoop Korattikara and Oscar Ramirez and Pablo Castro and Ethan Holly and Sam Fishman and Ke Wang and Ekaterina Gonina and Neal Wu and Efi Kokiopoulou and Luciano Sbaiz and Jamie Smith and Gábor Bartók and Jesse Berent and Chris Harris and Vincent Vanhoucke and Eugene Brevdo}, howpublished = {\url{https://github.com/tensorflow/agents}}, url = "https://github.com/tensorflow/agents", year = 2018, note = "[Online; accessed 25-June-2019]" } </code></pre></div> <p dir="auto"><a id="user-content-disclaimer"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Disclaimer</h2><a id="user-content-disclaimer" class="anchor" aria-label="Permalink: Disclaimer" href="#disclaimer"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This is not an official Google product.</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="bOeqCqiHJBT+6xpeO1KaQc5mKVZ3qAwJJnctrGZxx0xy4MvQy9Hv4oZiVkDnQ0LHBhMitytLGnk+N2CsonPQUA==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning. </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:reinforcement-learning" href="/topics/reinforcement-learning" title="Topic: reinforcement-learning" data-view-component="true" class="topic-tag topic-tag-link"> reinforcement-learning </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:tensorflow" 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class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/tensorflow/agents/releases" data-view-component="true" class="Link--primary no-underline Link"> Releases </a></h2> <a class="Link--primary no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/tensorflow/agents/tags"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <span class="text-bold">17</span> <span class="color-fg-muted">tags</span> </a> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/orgs/tensorflow/packages?repo_name=agents" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center"> Packages <span title="0" hidden="hidden" data-view-component="true" class="Counter ml-1">0</span> </a></h2> <div class="text-small color-fg-muted" > No packages published <br> </div> </div> </div> <div class="BorderGrid-row" hidden> <div class="BorderGrid-cell"> <include-fragment src="/tensorflow/agents/used_by_list" accept="text/fragment+html"> </include-fragment> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/tensorflow/agents/graphs/contributors" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center"> Contributors <span title="132" data-view-component="true" class="Counter ml-1">132</span> </a></h2> <ul class="list-style-none d-flex flex-wrap mb-n2"> <li class="mb-2 mr-2" > <a href="https://github.com/oars" class="" data-hovercard-type="user" data-hovercard-url="/users/oars/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/1630982?s=64&v=4" alt="@oars" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/tfboyd" class="" data-hovercard-type="user" data-hovercard-url="/users/tfboyd/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/23486130?s=64&v=4" alt="@tfboyd" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/ebrevdo" class="" data-hovercard-type="user" data-hovercard-url="/users/ebrevdo/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/1794715?s=64&v=4" alt="@ebrevdo" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/bartokg" class="" data-hovercard-type="user" data-hovercard-url="/users/bartokg/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/52548459?s=64&v=4" alt="@bartokg" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/sguada" class="" data-hovercard-type="user" data-hovercard-url="/users/sguada/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/1766524?s=64&v=4" alt="@sguada" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/summer-yue" class="" data-hovercard-type="user" data-hovercard-url="/users/summer-yue/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/8755896?s=64&v=4" alt="@summer-yue" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/kbanoop" class="" data-hovercard-type="user" data-hovercard-url="/users/kbanoop/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/31777471?s=64&v=4" alt="@kbanoop" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/efiko" class="" data-hovercard-type="user" data-hovercard-url="/users/efiko/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/44050735?s=64&v=4" alt="@efiko" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/samfishman" class="" data-hovercard-type="user" data-hovercard-url="/users/samfishman/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/184140?s=64&v=4" alt="@samfishman" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/egonina" class="" data-hovercard-type="user" data-hovercard-url="/users/egonina/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/3441503?s=64&v=4" alt="@egonina" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/nealwu" class="" data-hovercard-type="user" data-hovercard-url="/users/nealwu/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/726075?s=64&v=4" alt="@nealwu" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/agreenb" class="" data-hovercard-type="user" data-hovercard-url="/users/agreenb/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/3794748?s=64&v=4" alt="@agreenb" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/ormandi" class="" data-hovercard-type="user" data-hovercard-url="/users/ormandi/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/52251610?s=64&v=4" alt="@ormandi" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> </ul> <div data-view-component="true" class="mt-3"> <a text="small" href="/tensorflow/agents/graphs/contributors" data-view-component="true" class="Link--inTextBlock Link"> + 118 contributors </a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3">Languages</h2> <div class="mb-2"> <span data-view-component="true" class="Progress"> <span style="background-color:#3572A5 !important;;width: 99.8%;" itemprop="keywords" aria-label="Python 99.8" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#89e051 !important;;width: 0.2%;" itemprop="keywords" aria-label="Shell 0.2" data-view-component="true" class="Progress-item 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dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>