tf-agents：tensorflow的强化学习库
tf-agents-nightl的Python项目详细描述
TF-Agents：TensorFlow中的强化学习库
注意：当前TF代理的预发布正在积极开发中，并且 接口可以随时更改请随时提供反馈和意见。
为了开始，我们建议查看我们的colab教程之一。如果你 需要一个介绍rl（或快速回顾）。 start here。否则，请查看我们的 DQN tutorial让一个特工起来 在车柱环境中跑步。
目录
Agents
Tutorials
Examples
Installation
Contributing
Principles
Citation
Disclaimer
Agents
In TF-Agents, the core elements of RL algorithms are implemented as ^{
Currently the following algorithms are available under TF-Agents:
- DQN: Human level control through deep reinforcement learning Mnih et al., 2015
- DDQN: Deep Reinforcement Learning with Double Q-learning Hasselt et al., 2015
- DDPG: Continuous control with deep reinforcement learning Lillicrap et al., 2015
- TD3: Addressing Function Approximation Error in Actor-Critic Methods Fujimoto et al., 2018
- REINFORCE: Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning Williams, 1992
- PPO: Proximal Policy Optimization Algorithms Schulman et al., 2017
- SAC: Soft Actor Critic Haarnoja et al., 2018
Tutorials
See ^{
Examples
End-to-end examples training agents can be found under each agent directory. e.g.:
- DQN: ^{
}
Installation
To install the latest version, use nightly builds of TF-Agents under the pip package
^{
To install the nightly build version, run the following:
^{pr 1}$If you clone the repository you will still need a ^{
Contributing
We're eager to collaborate with you! See ^{
Principles
This project adheres to Google's AI principles。 通过参与、使用或参与本项目 坚持这些原则。
引文
如果您使用此代码，请将其引用为：
@misc{TFAgents,
title = {{TF-Agents}: A library for Reinforcement Learning in TensorFlow},
author = "{Sergio Guadarrama, Anoop Korattikara, Oscar Ramirez,
Pablo Castro, Ethan Holly, Sam Fishman, Ke Wang, Ekaterina Gonina, Neal Wu,
Chris Harris, Vincent Vanhoucke, Eugene Brevdo}",
howpublished = {\url{https://github.com/tensorflow/agents}},
url = "https://github.com/tensorflow/agents",
year = 2018,
note = "[Online; accessed 25-June-2019]"
}
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