AI prompts
base on Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学 <p align="center">
<a href="https://www.youtube.com/watch?v=pieI7rOXELI&list=PLXO45tsB95cIplu-fLMpUEEZTwrDNh6Ba" target="_blank">
<img width="60%" src="https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/blob/master/RL_cover.jpg" style="max-width:100%;">
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<br>
# 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)
<a href="contents/5_Deep_Q_Network">
<img class="course-image" src="https://mofanpy.com/static/results/reinforcement-learning/4-3-2.png">
</a>
### [Double DQN](contents/5.1_Double_DQN)
<a href="contents/5.1_Double_DQN">
<img class="course-image" src="https://mofanpy.com/static/results/reinforcement-learning/4-5-3.png">
</a>
### [Dueling DQN](contents/5.3_Dueling_DQN)
<a href="contents/5.3_Dueling_DQN">
<img class="course-image" src="https://mofanpy.com/static/results/reinforcement-learning/4-7-4.png">
</a>
### [Actor Critic](contents/8_Actor_Critic_Advantage)
<a href="contents/8_Actor_Critic_Advantage">
<img class="course-image" src="https://mofanpy.com/static/results/reinforcement-learning/6-1-1.png">
</a>
### [Deep Deterministic Policy Gradient](contents/9_Deep_Deterministic_Policy_Gradient_DDPG)
<a href="contents/9_Deep_Deterministic_Policy_Gradient_DDPG">
<img class="course-image" src="https://mofanpy.com/static/results/reinforcement-learning/6-2-2.png">
</a>
### [A3C](contents/10_A3C)
<a href="contents/10_A3C">
<img class="course-image" src="https://mofanpy.com/static/results/reinforcement-learning/6-3-2.png">
</a>
### [Proximal Policy Optimization (PPO)](contents/12_Proximal_Policy_Optimization)
<a href="contents/12_Proximal_Policy_Optimization">
<img class="course-image" src="https://mofanpy.com/static/results/reinforcement-learning/6-4-3.png">
</a>
### [Curiosity Model](/contents/Curiosity_Model)
<a href="/contents/Curiosity_Model">
<img class="course-image" src="/contents/Curiosity_Model/Curiosity.png">
</a>
# Donation
*If this does help you, please consider donating to support me for better tutorials. Any contribution is greatly appreciated!*
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", Assign "at most 3 tags" to the expected json: {"id":"4604","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"