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Hands-On-Intelligent-Agents-with-OpenAI-Gym

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Code for Hands On Intelligent Agents with OpenAI Gym book to get started and learn to build deep reinforcement learning agents using PyTorch

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- pytorch_test.py
- test_box2d.py
- get_observation_action_space.py
- list_gym_envs.py
- rl_gym_boilerplate_code.py
- run_gym_env.py
- Q_learner_MountainCar.py
- rl_gym_boilerplate_code_v2.py
- - __init__.py
  - atari.py
  - utils.py
- - __init__.py
  - cnn.py
  - perceptron.py
- - DQL_AlienNoFrameskip-v4.ptm
  - DQL_BreakoutNoFrameskip-v4.ptm
  - DQL_EnduroNoFrameskip-v4.ptm
  - DQL_RiverraidNoFrameskip-v4.ptm
  - DQL_SeaquestNoFrameskip-v4.ptm
  - DQL_SpaceInvadersNoFrameskip-v4.ptm
- - decay_schedule.py
  - experience_memory.py
  - params_manager.py
  - weights_initializer.py
- deep_Q_learner.py
- parameters.json
- README.md
- README.md
- - __init__.py
  - atari.py
  - utils.py
- - __init__.py
  - deep.py
  - shallow.py
- - A2C_Acrobot-v1.ptm
  - A2C_BipedalWalker-v2.ptm
  - A2C_Carla-v0.ptm
  - A2C_Carla-v0.ptm.agent_params
  - A2C_LunarLander-v2.ptm
  - A2C_Pendulum-v0.ptm
  - README.md
- - params_manager.py
- a2c_agent.py
- a2c_parameters.json
- async_a2c_agent.py
- async_a2c_parameters.json
- batched_a2c_agent.py
- README.md
- - .gitignore
- generate_env_clips.sh
- README.md
- run_roboschool_env.py
- setup_deepmindlab.sh
- setup_roboschool.sh
- - setup.sh
  - setup_test.py
- README.md
.gitignore
conda_env.yaml
LICENSE
README.md

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# Hands-on Intelligent Agents with OpenAI Gym (HOIAWOG)

The Book          |  Examples of agents you will learn to develop
:-------------------------:|:-------------------------:
[<img src="https://static.packt-cdn.com/products/9781788836579/cover/smaller" /></br>](https://www.packtpub.com/big-data-and-business-intelligence/hands-intelligent-agents-openai-gym) [ Topics Covered](https://praveenp.com/hands-on-intelligent-agents-with-openai-gym-hoiawog/)|  [![](https://user-images.githubusercontent.com/4770482/149641959-4685b4bb-6402-44df-a2d8-95a13b21d328.gif)](https://praveenp.com/deeprl/2018/12/12/hoiawog.html)

**HOIAWOG!: Your guide to developing AI agents using deep reinforcement learning**. Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator.

![Chapter 8 demo](https://user-images.githubusercontent.com/4770482/149642030-9a5fc2b0-9268-45cc-9328-04f2b2e423e6.gif)
<a class="ba-award" href="https://bookauthority.org/books/best-reinforcement-learning-ebooks?t=1a0g37&s=award&book=178883657X" target="_blank" style="margin:20px; outline:0"><img src="https://award.bookauthority.org/best-reinforcement-learning-ebooks.png?b=178883657X&c=1&v=6&w=200" style="width:200px; height:183px; border:0" alt="BookAuthority Best Reinforcement Learning eBooks of All Time"/></a>
### Chapter list:
(Click to learn more)
- Chapter 1: Introduction to Intelligent Agents and Learning Environments :space_invader:
- Chapter 2: Reinforcement Learning and Deep Reinforcement Learning 
- [Chapter 3: Getting started with OpenAI Gym and Deep Reinforcement Learning](https://github.com/PacktPublishing/Hands-On-Intelligent-Agents-with-OpenAI-Gym/tree/master/ch3)
- [Chapter 4: Exploring the Gym and its features](https://github.com/PacktPublishing/Hands-On-Intelligent-Agents-with-OpenAI-Gym/tree/master/ch4)
- [Chapter 5: Implementing your first learning agent -- Solving the Mountain Car problem](https://github.com/PacktPublishing/Hands-On-Intelligent-Agents-with-OpenAI-Gym/tree/master/ch5)
- [Chapter 6: Implementing an Intelligent Agent for Optimal Control using Deep Q Learning](https://github.com/PacktPublishing/Hands-On-Intelligent-Agents-with-OpenAI-Gym/tree/master/ch6)
- [Chapter 7: Creating custom OpenAI gym environments - Carla driving simulator](https://github.com/PacktPublishing/Hands-On-Intelligent-Agents-with-OpenAI-Gym/tree/master/ch7)
- [Chapter 8: Implementing an Intelligent & Autonomous Car Driving Agent using Deep Actor-Critic Algorithm](https://github.com/PacktPublishing/Hands-On-Intelligent-Agents-with-OpenAI-Gym/tree/master/ch8)
- [Chapter 9: Exploring the Learning Environment Landscape: Roboschool, Gym-Retro, StarCraft-II, DeepMindLab](https://github.com/PacktPublishing/Hands-On-Intelligent-Agents-with-OpenAI-Gym/tree/master/ch9)
- Chapter 10: Exploring the Learning Algorithm Landscape: DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-based)

## Citing

If you use the code samples in your work or want to cite the book, please use:

```bibtex
@book{Palanisamy:2018:HIA:3285236,
 author = {Palanisamy, Praveen},
 title = {Hands-On Intelligent Agents with OpenAI Gym: Your Guide to Developing AI Agents Using Deep Reinforcement Learning},
 year = {2018},
 isbn = {178883657X, 9781788836579},
 publisher = {Packt Publishing},
}
```

<details><summary>Other Formats: (Click to View)</summary>
<p>
<div id="gs_citd" aria-live="assertive" data-u="/scholar?q=info:{id}:scholar.google.com/&amp;output=cite&amp;scirp={p}&amp;scfhb=1&amp;hl=en"><div id="gs_citt"><table><tbody><tr><th scope="row" class="gs_cith">MLA</th><td><div tabindex="0" class="gs_citr">Palanisamy, Praveen. <i>Hands-On Intelligent Agents with OpenAI Gym: Your guide to developing AI agents using deep reinforcement learning</i>. Packt Publishing Ltd, 2018.</div></td></tr><tr><th scope="row" class="gs_cith">APA</th><td><div tabindex="0" class="gs_citr">Palanisamy, P. (2018). <i>Hands-On Intelligent Agents with OpenAI Gym: Your guide to developing AI agents using deep reinforcement learning</i>. Packt Publishing Ltd.</div></td></tr><tr><th scope="row" class="gs_cith">Chicago</th><td><div tabindex="0" class="gs_citr">Palanisamy, Praveen. <i>Hands-On Intelligent Agents with OpenAI Gym: Your guide to developing AI agents using deep reinforcement learning</i>. Packt Publishing Ltd, 2018.</div></td></tr><tr><th scope="row" class="gs_cith">Harvard</th><td><div tabindex="0" class="gs_citr">Palanisamy, P., 2018. <i>Hands-On Intelligent Agents with OpenAI Gym: Your guide to developing AI agents using deep reinforcement learning</i>. Packt Publishing Ltd.</div></td></tr><tr><th scope="row" class="gs_cith">Vancouver</th><td><div tabindex="0" class="gs_citr">Palanisamy P. Hands-On Intelligent Agents with OpenAI Gym: Your guide to developing AI agents using deep reinforcement learning. Packt Publishing Ltd; 2018 Jul 31.</div></td></tr></tbody></table></div><div id="gs_citi"><a class="gs_citi" href="https://scholar.googleusercontent.com/scholar.bib?q=info:nrTsCNc4NuEJ:scholar.google.com/&amp;output=citation&amp;scisig=AAGBfm0AAAAAXHdbjGz9YaWF_UA2OsNp3HBmEISQx3pW&amp;scisf=4&amp;ct=citation&amp;cd=-1&amp;hl=en&amp;scfhb=1">BibTeX</a> <a class="gs_citi" href="https://scholar.googleusercontent.com/scholar.enw?q=info:nrTsCNc4NuEJ:scholar.google.com/&amp;output=citation&amp;scisig=AAGBfm0AAAAAXHdbjGz9YaWF_UA2OsNp3HBmEISQx3pW&amp;scisf=3&amp;ct=citation&amp;cd=-1&amp;hl=en&amp;scfhb=1">EndNote</a> <a class="gs_citi" href="https://scholar.googleusercontent.com/scholar.ris?q=info:nrTsCNc4NuEJ:scholar.google.com/&amp;output=citation&amp;scisig=AAGBfm0AAAAAXHdbjGz9YaWF_UA2OsNp3HBmEISQx3pW&amp;scisf=2&amp;ct=citation&amp;cd=-1&amp;hl=en&amp;scfhb=1">RefMan</a> <a class="gs_citi" href="https://scholar.googleusercontent.com/scholar.rfw?q=info:nrTsCNc4NuEJ:scholar.google.com/&amp;output=citation&amp;scisig=AAGBfm0AAAAAXHdbjGz9YaWF_UA2OsNp3HBmEISQx3pW&amp;scisf=1&amp;ct=citation&amp;cd=-1&amp;hl=en&amp;scfhb=1" target="RefWorksMain">RefWorks</a> </div></div>
</p>
</details>
### Download a free PDF

<i>If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.<br>Simply click on the link to claim your free PDF.</i>
<p align="center"> <a href="https://packt.link/free-ebook/9781788836579">https://packt.link/free-ebook/9781788836579 </a> </p>