Reinforcement-learning-with-tensorflow-master
说明： 各种强化学习代码，强化学习(reinforcement learning)，又称再励学习、评价学习，是一种重要的机器学习方法，在智能控制机器人及分析预测等领域有许多应用。
(Reinforcement learning (reinforcement learning), also known as reinforcement learning and evaluation learning, is an important machine learning method. It has many applications in intelligent control robots, analysis and prediction.)
(Reinforcement learning (reinforcement learning), also known as reinforcement learning and evaluation learning, is an important machine learning method. It has many applications in intelligent control robots, analysis and prediction.)
文件列表:
contents\10_A3C\A3C_continuous_action.py (8150, 2017-09-13)
contents\10_A3C\A3C_discrete_action.py (7897, 2017-09-13)
contents\10_A3C\A3C_RNN.py (9425, 2017-09-13)
contents\11_Dyna_Q\maze_env.py (3898, 2017-09-13)
contents\11_Dyna_Q\RL_brain.py (2915, 2017-09-13)
contents\11_Dyna_Q\run_this.py (1500, 2017-09-13)
contents\12_Proximal_Policy_Optimization\DPPO.py (8263, 2017-09-13)
contents\12_Proximal_Policy_Optimization\simply_PPO.py (6465, 2017-09-13)
contents\1_command_line_reinforcement_learning\treasure_on_right.py (3346, 2017-09-13)
contents\2_Q_Learning_maze\maze_env.py (4251, 2017-09-13)
contents\2_Q_Learning_maze\RL_brain.py (1814, 2017-09-13)
contents\2_Q_Learning_maze\run_this.py (1388, 2017-09-13)
contents\3_Sarsa_maze\maze_env.py (3956, 2017-09-13)
contents\3_Sarsa_maze\RL_brain.py (2671, 2017-09-13)
contents\3_Sarsa_maze\run_this.py (1510, 2017-09-13)
contents\4_Sarsa_lambda_maze\maze_env.py (3957, 2017-09-13)
contents\4_Sarsa_lambda_maze\RL_brain.py (3138, 2017-09-13)
contents\4_Sarsa_lambda_maze\run_this.py (1602, 2017-09-13)
contents\5.1_Double_DQN\RL_brain.py (6678, 2017-09-13)
contents\5.1_Double_DQN\run_Pendulum.py (2133, 2017-09-13)
contents\5.2_Prioritized_Replay_DQN\RL_brain.py (11847, 2017-09-13)
contents\5.2_Prioritized_Replay_DQN\run_MountainCar.py (2113, 2017-09-13)
contents\5.3_Dueling_DQN\RL_brain.py (6714, 2017-09-13)
contents\5.3_Dueling_DQN\run_Pendulum.py (2159, 2017-09-13)
contents\5_Deep_Q_Network\DQN_modified.py (6537, 2017-09-13)
contents\5_Deep_Q_Network\maze_env.py (4165, 2017-09-13)
contents\5_Deep_Q_Network\RL_brain.py (8416, 2017-09-13)
contents\5_Deep_Q_Network\run_this.py (1342, 2017-09-13)
contents\6_OpenAI_gym\RL_brain.py (8416, 2017-09-13)
contents\6_OpenAI_gym\run_CartPole.py (1475, 2017-09-13)
contents\6_OpenAI_gym\run_MountainCar.py (1355, 2017-09-13)
contents\7_Policy_gradient_softmax\RL_brain.py (4346, 2017-09-13)
contents\7_Policy_gradient_softmax\run_CartPole.py (1815, 2017-09-13)
contents\7_Policy_gradient_softmax\run_MountainCar.py (2025, 2017-09-13)
contents\8_Actor_Critic_Advantage\AC_CartPole.py (5860, 2017-09-13)
contents\8_Actor_Critic_Advantage\AC_continue_Pendulum.py (6405, 2017-09-13)
contents\9_Deep_Deterministic_Policy_Gradient_DDPG\DDPG.py (9931, 2017-09-13)
contents\9_Deep_Deterministic_Policy_Gradient_DDPG\DDPG_update.py (5755, 2017-09-13)
experiments\2D_car\car_env.py (7953, 2017-09-13)
experiments\2D_car\collision.py (2072, 2017-09-13)
... ...
contents\10_A3C\A3C_discrete_action.py (7897, 2017-09-13)
contents\10_A3C\A3C_RNN.py (9425, 2017-09-13)
contents\11_Dyna_Q\maze_env.py (3898, 2017-09-13)
contents\11_Dyna_Q\RL_brain.py (2915, 2017-09-13)
contents\11_Dyna_Q\run_this.py (1500, 2017-09-13)
contents\12_Proximal_Policy_Optimization\DPPO.py (8263, 2017-09-13)
contents\12_Proximal_Policy_Optimization\simply_PPO.py (6465, 2017-09-13)
contents\1_command_line_reinforcement_learning\treasure_on_right.py (3346, 2017-09-13)
contents\2_Q_Learning_maze\maze_env.py (4251, 2017-09-13)
contents\2_Q_Learning_maze\RL_brain.py (1814, 2017-09-13)
contents\2_Q_Learning_maze\run_this.py (1388, 2017-09-13)
contents\3_Sarsa_maze\maze_env.py (3956, 2017-09-13)
contents\3_Sarsa_maze\RL_brain.py (2671, 2017-09-13)
contents\3_Sarsa_maze\run_this.py (1510, 2017-09-13)
contents\4_Sarsa_lambda_maze\maze_env.py (3957, 2017-09-13)
contents\4_Sarsa_lambda_maze\RL_brain.py (3138, 2017-09-13)
contents\4_Sarsa_lambda_maze\run_this.py (1602, 2017-09-13)
contents\5.1_Double_DQN\RL_brain.py (6678, 2017-09-13)
contents\5.1_Double_DQN\run_Pendulum.py (2133, 2017-09-13)
contents\5.2_Prioritized_Replay_DQN\RL_brain.py (11847, 2017-09-13)
contents\5.2_Prioritized_Replay_DQN\run_MountainCar.py (2113, 2017-09-13)
contents\5.3_Dueling_DQN\RL_brain.py (6714, 2017-09-13)
contents\5.3_Dueling_DQN\run_Pendulum.py (2159, 2017-09-13)
contents\5_Deep_Q_Network\DQN_modified.py (6537, 2017-09-13)
contents\5_Deep_Q_Network\maze_env.py (4165, 2017-09-13)
contents\5_Deep_Q_Network\RL_brain.py (8416, 2017-09-13)
contents\5_Deep_Q_Network\run_this.py (1342, 2017-09-13)
contents\6_OpenAI_gym\RL_brain.py (8416, 2017-09-13)
contents\6_OpenAI_gym\run_CartPole.py (1475, 2017-09-13)
contents\6_OpenAI_gym\run_MountainCar.py (1355, 2017-09-13)
contents\7_Policy_gradient_softmax\RL_brain.py (4346, 2017-09-13)
contents\7_Policy_gradient_softmax\run_CartPole.py (1815, 2017-09-13)
contents\7_Policy_gradient_softmax\run_MountainCar.py (2025, 2017-09-13)
contents\8_Actor_Critic_Advantage\AC_CartPole.py (5860, 2017-09-13)
contents\8_Actor_Critic_Advantage\AC_continue_Pendulum.py (6405, 2017-09-13)
contents\9_Deep_Deterministic_Policy_Gradient_DDPG\DDPG.py (9931, 2017-09-13)
contents\9_Deep_Deterministic_Policy_Gradient_DDPG\DDPG_update.py (5755, 2017-09-13)
experiments\2D_car\car_env.py (7953, 2017-09-13)
experiments\2D_car\collision.py (2072, 2017-09-13)
... ...
# 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://morvanzhou.github.io/tutorials/) 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](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/1_command_line_reinforcement_learning) * [Q-learning](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/2_Q_Learning_maze) * [Sarsa](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/3_Sarsa_maze) * [Sarsa(lambda)](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/4_Sarsa_lambda_maze) * [Deep Q Network](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5_Deep_Q_Network) * [Using OpenAI Gym](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/6_OpenAI_gym) * [Double DQN](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5.1_Double_DQN) * [DQN with Prioitized Experience Replay](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5.2_Prioritized_Replay_DQN) * [Dueling DQN](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/5.3_Dueling_DQN) * [Policy Gradients](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/7_Policy_gradient_softmax) * [Actor Critic](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/8_Actor_Critic_Advantage) * [Deep Deterministic Policy Gradient](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/9_Deep_Deterministic_Policy_Gradient_DDPG) * [A3C](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/10_A3C) * [Dyna-Q](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/11_Dyna_Q) * [Proximal Policy Optimization (PPO)](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/contents/12_Proximal_Policy_Optimization) * [Some of my experients](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/experiments) * [2D Car](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/experiments/2D_car) * [Robot arm](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/experiments/Robot_arm) * [BipedalWalker](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/experiments/Solve_BipedalWalker) * [LunarLander](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/tree/master/experiments/Solve_LunarLander) # Donation *If this does help you, please consider donating to support me for better tutorials. Any contribution is greatly appreciated!*
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