<commit_before># Reinforcement Learning Methods and Tutorials

In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.

All methods mentioned below have their video and text tutorial in Chinese. Visit [莫烦 Python](https://morvanzhou.github.io/tutorials/) for more.
If you speak Chinese, you can watch my [Youtube channel](https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg) as well.

**As many requests about making these tutorials available in English, please find them in this playlist:** ([https://www.youtube.com/playlist?list=PLXO45tsB95cIplu-fLMpUEEZTwrDNh6Ba](https://www.youtube.com/playlist?list=PLXO45tsB95cIplu-fLMpUEEZTwrDNh6Ba))


* [Simple entry example](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/1_command_line_reinforcement_learning)
* Tabular Methods
  * [Q-learning](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/2_Q_Learning_maze)
  * [Sarsa](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/3_Sarsa_maze)
  * [Sarsa(lambda)](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/4_Sarsa_lambda_maze)
* Function Approximation (DQN)
  * [Deep Q Network](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/5_Deep_Q_Network)
* [Using OpenAI Gym](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/6_OpenAI_gym)
* DQN-based methods
  * [Double DQN](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/5.1_Double_DQN)
  * [DQN with Prioitized Experience Replay](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/5.2_Prioritized_Replay_DQN)
  * [Dueling DQN](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/5.3_Dueling_DQN)
* [Policy Gradients](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/7_Policy_gradient_softmax)
* [Actor Critic](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/8_Actor_Critic_Advantage)
  * [Deep Deterministic Policy Gradient](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/9_Deep_Deterministic_Policy_Gradient_DDPG)
  * [A3C](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/10_A3C)
* Model-based RL (WIP)
  * [Dyna-Q](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/11_Dyna_Q)


# Donation

*If this does help you, please considering donation to support me to make better tutorials*

<div >
  <h3>Paypal</h3>
  <a href="https://www.paypal.com/cgi-bin/webscr?cmd=_donations&amp;business=morvanzhou%40gmail%2ecom&amp;lc=C2&amp;item_name=MorvanPython&amp;currency_code=AUD&amp;bn=PP%2dDonationsBF%3abtn_donateCC_LG%2egif%3aNonHosted"><commit_msg>support<commit_after># Reinforcement Learning Methods and Tutorials

In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.

All methods mentioned below have their video and text tutorial in Chinese. Visit [莫烦 Python](https://morvanzhou.github.io/tutorials/) for more.
If you speak Chinese, you can watch my [Youtube channel](https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg) as well.

**As many requests about making these tutorials available in English, please find them in this playlist:** ([https://www.youtube.com/playlist?list=PLXO45tsB95cIplu-fLMpUEEZTwrDNh6Ba](https://www.youtube.com/playlist?list=PLXO45tsB95cIplu-fLMpUEEZTwrDNh6Ba))


* [Simple entry example](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/1_command_line_reinforcement_learning)
* Tabular Methods
  * [Q-learning](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/2_Q_Learning_maze)
  * [Sarsa](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/3_Sarsa_maze)
  * [Sarsa(lambda)](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/4_Sarsa_lambda_maze)
* Function Approximation (DQN)
  * [Deep Q Network](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/5_Deep_Q_Network)
* [Using OpenAI Gym](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/6_OpenAI_gym)
* DQN-based methods
  * [Double DQN](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/5.1_Double_DQN)
  * [DQN with Prioitized Experience Replay](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/5.2_Prioritized_Replay_DQN)
  * [Dueling DQN](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/5.3_Dueling_DQN)
* [Policy Gradients](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/7_Policy_gradient_softmax)
* [Actor Critic](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/8_Actor_Critic_Advantage)
  * [Deep Deterministic Policy Gradient](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/9_Deep_Deterministic_Policy_Gradient_DDPG)
  * [A3C](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/10_A3C)
* Model-based RL (WIP)
  * [Dyna-Q](https://github.com/MorvanZhou/tutorials/tree/master/Reinforcement_learning_TUT/11_Dyna_Q)


# Donation

*If this does help you, please considering donation to support me to make better tutorials*

<div >
  <a href="https://www.paypal.com/cgi-bin/webscr?cmd=_donations&amp;business=morvanzhou%40gmail%2ecom&amp;lc=C2&amp;item_name=MorvanPython&amp;currency_code=AUD&amp;bn=PP%2dDonationsBF%3abtn_donateCC_LG%2egif%3aNonHosted">
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