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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
| Date | Stars |
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| 2026-07-31 | 399 |
| 2026-08-01 | 399 |
| 2026-08-02 | 399 |
| 2026-08-06 | 399 |
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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://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.
<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/&output=cite&scirp={p}&scfhb=1&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">PalanisaExcerpt of 6,454 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:36b353eb6e79082b, topic:deep-reinforcement-learning, topic:openai-gym, desc:reinforcement learning