# 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!*