Reinforcement_Learning

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Reinforcement_Learning

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Reinforcement learning tutorials

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최종 버전 다운로드 (.zip)

- - CartPole_test.gif
  - image.png
  - math.PNG
  - testing_model.PNG
  - training_model.PNG
- cartpole-dqn.h5
- Cartpole_DQN.py
- cartpole_random.py
- - DDQN_CartPole-v1.png
  - DDQN_CartPole-v1_soft.png
  - DQN_CartPole-v1.png
- Cartpole_DDQN.py
- Cartpole_DDQN_TF2.py
- - DDQN_CartPole-v1.png
  - DDQN_CartPole-v1_Dueling.png
  - DQN_CartPole-v1_Dueling.png
- Cartpole_Double_DDQN.py
- Cartpole_Double_DDQN_TF2.py
- - DDQN_CartPole-v1_Dueling_Greedy.png
- Cartpole_e_greedy_D3QN.py
- Cartpole_e_greedy_D3QN_TF2.py
- - DDQN_CartPole-v1_Dueling.png
  - DDQN_CartPole-v1_Dueling_PER.png
  - Replay_buffer.png
  - SumTree.png
- Cartpole_PER_D3QN.py
- Cartpole_PER_D3QN_TF2.py
- PER.py
- Cartpole_PER_D3QN_CNN.py
- Cartpole_PER_D3QN_CNN_TF2.py
- PER.py
- random_game.py
- - DDQN_Pong-v0_CNN.png
  - DDQN_Pong-v0_Dueling_CNN.png
  - DDQN_Pong-v0_Dueling_PER_CNN.png
  - DQN_Pong-v0_CNN.png
- - Pong-v0_DDQN_CNN.h5
  - Pong-v0_DDQN_Dueling_CNN.h5
  - Pong-v0_DDQN_Dueling_PER_CNN.h5
  - Pong-v0_DQN_CNN.h5
- PER.py
- Pong-v0_DQN_CNN.py
- Pong-v0_DQN_CNN_TF2.py
- - Pong-v0_PG_2.5e-05.png
  - PongDeterministic-v4_PG_0.0001.png
- Pong-v0_PG.py
- Pong-v0_PG_TF2.py
- - Pong-v0_A2C_2.5e-05.png
  - PongDeterministic-v4_A2C_2.5e-05.png
- Pong-v0_A2C.py
- Pong-v0_A2C_TF2.py
- Pong-v0_A3C.py
- Pong-v0_A3C_TF2.py
- PongDeterministic-v4_A3C_2.5e-05.png
- - Pong-v0_APPO_0.0001_Actor_CNN.h5
- gameplay.gif
- gameplay_CNN.gif
- Pong-v0_APPO_0.0001_CNN.png
- Pong-v0_APPO_0.0001_RMSprop.png
- Pong-v0_PPO.py
- Pong-v0_PPO_gif.py
- Pong-v0_PPO_TF2.py
- PongDeterministic-v4_APPO_0.0001.png
- BipedalWalker-v3_PPO.py
- BipedalWalker-v3_PPO_Actor.h5
- BipedalWalker-v3_PPO_Critic.h5
- BipedalWalker-v3_training.png
- gameplay.gif
- gameplay.gif
- LunarLander-v2.png
- LunarLander-v2_PPO.py
- LunarLander-v2_PPO_Actor.h5
- LunarLander-v2_PPO_Critic.h5
LICENSE.md
README.md
requirements.txt

// repository documentation

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# Reinforcement Learning Tutorials:

*2020-10-07 added support for Tensorflow 2.3.1*

PPO and PPO_CNN agents playing Pong-v0 game:<br>
![PPO agent](https://raw.githubusercontent.com/pythonlessons/Reinforcement_Learning/master/11_Pong-v0_PPO/gameplay.gif)
![PPO CNN agent](https://raw.githubusercontent.com/pythonlessons/Reinforcement_Learning/master/11_Pong-v0_PPO/gameplay_CNN.gif)

*2020-10-10 added LunarLander-v2_PPO Continuous code for Tensorflow 2.3.1*:
![LunarLander-v2_PPO](https://github.com/pythonlessons/Reinforcement_Learning/blob/master/LunarLander-v2_PPO/gameplay.gif)

*2020-10-23 added BipedalWalker-v3_PPO code for Tensorflow 2.3.1*:
![BipedalWalker-v3_PPO_PPO](https://github.com/pythonlessons/Reinforcement_Learning/blob/master/BipedalWalker-v3_PPO/gameplay.gif)

1. [Deep Q Learning tutorial (DQN)](https://pylessons.com/CartPole-reinforcement-learning/)

2. [Double Deep Q Learning tutorial (DDQN)](https://pylessons.com/CartPole-DDQN/)

3. [Dueling Double Deep Q Learning tutorial (D3QN)](https://pylessons.com/CartPole-DDDQN/)

4. [Epsilon Greedy Dueling Double Deep Q Learning tutorial (D3QN)](https://pylessons.com/Epsilon-Greedy-DQN/)

5. [Prioritized Experience Replay (PER) D3QN tutorial](https://pylessons.com/CartPole-PER/)

6. [D3QN PER with Convolutional Neural Networks tutorial](https://pylessons.com/CartPole-PER-CNN/)

7. [A.I. learns to play Pong with DQN](https://pylessons.com/DQN-PONG/)

8. [Introduction to RL Policy Gradient (PG or REINFORCE)](https://pylessons.com/Beyond-DQN/)

9. [Introduction to RL Advanced Actor Critic algorythm (A2C)](https://pylessons.com/A2C-reinforcement-learning/)

10. [Introduction to RL Asynchronous Advanced Actor Critic algorythm (A3C)](https://pylessons.com/A3C-reinforcement-learning/)

11. [Introduction to RL Proximal Policy Optimization algorythm (PPO)](https://pylessons.com/PPO-reinforcement-learning/)

12. [Let’s code from scratch a discrete Reinforcement Learning rocket landing agent! (PPO)](https://pylessons.com/LunarLander-v2-PPO/)

13. [Continuous Proximal Policy Optimization Tutorial with OpenAI gym environment! (PPO)](https://pylessons.com/BipedalWalker-v3-PPO/)
<br><br>
PPO Pong-v0 Learning curve:
<img src="11_Pong-v0_PPO/Pong-v0_APPO_0.0001_RMSprop.png" data-canonical-src="11_Pong-v0_PPO/Pong-v0_APPO_0.0001_RMSprop.png" width="500" height="300" />

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