https://github.com/morvanzhou/reinforcement-learning-with-tensorflow

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
https://github.com/morvanzhou/reinforcement-learning-with-tensorflow

a3c actor-critic asynchronous-advantage-actor-critic ddpg deep-deterministic-policy-gradient deep-q-network double-dqn dqn dueling-dqn machine-learning policy-gradient ppo prioritized-replay proximal-policy-optimization q-learning reinforcement-learning sarsa sarsa-lambda tensorflow-tutorials tutorial

Last synced: about 3 hours ago
JSON representation

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

• Host: GitHub
• URL: https://github.com/morvanzhou/reinforcement-learning-with-tensorflow
• Owner: MorvanZhou
• License: mit
• Created: 2017-05-06T03:01:31.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2024-03-31T05:40:51.000Z (6 months ago)
• Last Synced: 2024-09-22T08:01:59.451Z (2 days ago)
• Topics: a3c, actor-critic, asynchronous-advantage-actor-critic, ddpg, deep-deterministic-policy-gradient, deep-q-network, double-dqn, dqn, dueling-dqn, machine-learning, policy-gradient, ppo, prioritized-replay, proximal-policy-optimization, q-learning, reinforcement-learning, sarsa, sarsa-lambda, tensorflow-tutorials, tutorial
• Language: Python
• Homepage: https://mofanpy.com/tutorials/machine-learning/reinforcement-learning/
• Size: 428 KB
• Stars: 8,858
• Watchers: 287
• Forks: 5,001
• Open Issues: 68
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        


    

    

    






# Reinforcement Learning Methods and Tutorials

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

**If you speak Chinese, visit [莫烦 Python](https://mofanpy.com) or my [Youtube channel](https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg) for more.**

**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))

# Table of Contents

* Tutorials

    * [Simple entry example](contents/1_command_line_reinforcement_learning)

    * [Q-learning](contents/2_Q_Learning_maze)

    * [Sarsa](contents/3_Sarsa_maze)

    * [Sarsa(lambda)](contents/4_Sarsa_lambda_maze)

    * [Deep Q Network (DQN)](contents/5_Deep_Q_Network)

    * [Using OpenAI Gym](contents/6_OpenAI_gym)

    * [Double DQN](contents/5.1_Double_DQN)

    * [DQN with Prioitized Experience Replay](contents/5.2_Prioritized_Replay_DQN)

    * [Dueling DQN](contents/5.3_Dueling_DQN)

    * [Policy Gradients](contents/7_Policy_gradient_softmax)

    * [Actor-Critic](contents/8_Actor_Critic_Advantage)

    * [Deep Deterministic Policy Gradient (DDPG)](contents/9_Deep_Deterministic_Policy_Gradient_DDPG)

    * [A3C](contents/10_A3C)

    * [Dyna-Q](contents/11_Dyna_Q)

    * [Proximal Policy Optimization (PPO)](contents/12_Proximal_Policy_Optimization)

    * [Curiosity Model](/contents/Curiosity_Model), [Random Network Distillation (RND)](/contents/Curiosity_Model/Random_Network_Distillation.py)

* [Some of my experiments](experiments)

    * [2D Car](experiments/2D_car)

    * [Robot arm](experiments/Robot_arm)

    * [BipedalWalker](experiments/Solve_BipedalWalker)

    * [LunarLander](experiments/Solve_LunarLander)

# Some RL Networks

### [Deep Q Network](contents/5_Deep_Q_Network)



    



### [Double DQN](contents/5.1_Double_DQN)



    



### [Dueling DQN](contents/5.3_Dueling_DQN)



    



### [Actor Critic](contents/8_Actor_Critic_Advantage)



    



### [Deep Deterministic Policy Gradient](contents/9_Deep_Deterministic_Policy_Gradient_DDPG)



    



### [A3C](contents/10_A3C)



    



### [Proximal Policy Optimization (PPO)](contents/12_Proximal_Policy_Optimization)



    



### [Curiosity Model](/contents/Curiosity_Model)



    



# Donation

*If this does help you, please consider donating to support me for better tutorials. Any contribution is greatly appreciated!*