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"description": "# TF-Agents: A library for Reinforcement Learning in TensorFlow\n\n*NOTE:* Current TF-Agents pre-release is under active development and\ninterfaces may change at any time. Feel free to provide feedback and comments.\n\nThe documentation, examples and tutorials will grow over the next few weeks.\n\n## Table of contents\n\nAgents
\nTutorials
\nExamples
\nInstallation
\nContributing
\nPrinciples
\nCitation
\nDisclaimer
\n\n\n\n## Agents\n\n\nIn TF-Agents, the core elements of RL algorithms are implemented as `Agents`.\nAn agent encompasses two main responsibilities: defining a Policy to interact\nwith the Environment, and how to learn/train that Policy from collected\nexperience.\n\nCurrently the following algorithms are available under TF-Agents:\n\n* DQN: __Human level control through deep reinforcement learning__ Mnih et al., 2015 https://deepmind.com/research/dqn/\n* DDQN: __Deep Reinforcement Learning with Double Q-learning__ Hasselt et al., 2015 https://arxiv.org/abs/1509.06461\n* DDPG: __Continuous control with deep reinforcement learning__ Lilicrap et al. https://arxiv.org/abs/1509.02971\n* TD3: __Addressing Function Approximation Error in Actor-Critic Methods__ Fujimoto et al. https://arxiv.org/abs/1802.09477.\n* REINFORCE: __Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning__ Williams http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf\n* PPO: __Proximal Policy Optimization Algorithms__ Schulman et al. http://arxiv.org/abs/1707.06347\n* SAC: __Soft Actor Critic__ Haarnoja et al. https://arxiv.org/abs/1812.05905\n\n\n## Tutorials\n\nSee [`tf_agents/colabs/`](https://github.com/tensorflow/agents/tree/master/tf_agents/colabs/)\nfor tutorials on the major components provided.\n\n\n## Examples\nEnd-to-end examples training agents can be found under each agent directory.\ne.g.:\n\n* DQN: [`tf_agents/agents/dqn/examples/train_eval_gym.py`](https://github.com/tensorflow/agents/tree/master/tf_agents/agents/dqn/examples/train_eval_gym.py)\n\n\n## Installation\n\nTo install the latest version, use nightly builds of TF-Agents under the pip package\n`tf-agents-nightly`, which requires you install on one of `tf-nightly` and\n`tf-nightly-gpu` and also `tensorflow-probability-nightly`.\nNightly builds include newer features, but may be less stable than the versioned releases.\n\nTo install the nightly build version, run the following:\n\n```shell\n# Installing with the `--upgrade` flag ensures you'll get the latest version.\npip install --user --upgrade tf-agents-nightly # depends on tf-nightly\n```\n\n\n## Contributing\n\nWe're eager to collaborate with you! See [`CONTRIBUTING.md`](CONTRIBUTING.md)\nfor a guide on how to contribute. This project adheres to TensorFlow's\n[code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to\nuphold this code.\n\n\n## Principles\n\nThis project adheres to [Google's AI principles](PRINCIPLES.md).\nBy participating, using or contributing to this project you are expected to\nadhere to these principles.\n\n\n## Citation\n\nIf you use this code please cite it as:\n\n```\n@misc{TFAgents,\n title = {{TF-Agents}: A library for Reinforcement Learning in TensorFlow},\n author = \"{Sergio Guadarrama, Anoop Korattikara, Oscar Ramirez,\n Pablo Castro, Ethan Holly, Sam Fishman, Ke Wang, Ekaterina Gonina,\n Chris Harris, Vincent Vanhoucke, Eugene Brevdo}\",\n howpublished = {\\url{https://github.com/tensorflow/agents}},\n url = \"https://github.com/tensorflow/agents\",\n year = 2018,\n note = \"[Online; accessed 30-November-2018]\"\n}\n```\n\n\n## Disclaimer\n\nThis is not an official Google product.\n\n\n",
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