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Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math
| Date | Stars |
|---|---|
| 2026-07-31 | 476 |
| 2026-08-02 | 476 |
| 2026-08-03 | 476 |
| 2026-08-06 | 476 |
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# [Deep Reinforcement Learning With Python](https://www.amazon.com/dp/1839210680/ref=cm_sw_r_tw_dp_x_avRDFb99EVTQ) ### Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math ## About the book <a target="_blank" href="https://www.amazon.com/dp/1839210680/ref=cm_sw_r_tw_dp_x_avRDFb99EVTQ"> <img src="./images/2.jpg" alt="Book Cover" width="300" align="left"/> </a>With significant enhancement in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been completely revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as the Bellman equation, Markov decision processes, and dynamic programming, this second edition dives deep into the full spectrum of value-based, policy-based, and actor- critic RL methods with detailed math. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage Stable Baselines, an improvement of OpenAI's baseline library, to implement popular RL algorithms effortlessly. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. ## Get the book <div> <a target="_blank" href="https://www.oreilly.com/library/view/deep-reinforcement-learning/9781839210686/"> <img src="./images/Oreilly_safari_logo.png" alt="Oreilly Safari" hieght=150, width=150> </a> <a target="_blank" href="https://www.amazon.com/gp/product/B08HSHV72N/ref=dbs_a_def_rwt_bibl_vppi_i2"> <img src="./images/amazon_logo.jpg" alt="Amazon" > </a> <a target="_blank" href="https://www.packtpub.com/product/deep-reinforcement-learning-with-python-second-edition/9781839210686"> <img src="./images/packt_logo.jpeg" alt="Packt" hieght=150, width=150 > </a> <a target="_blank" href="https://www.google.co.in/books/edition/Deep_Reinforcement_Learning_with_Python/dFkAEAAAQBAJ?hl=en&gbpv=0&kptab=overview"> <img src="./images/googlebooks_logo.png" alt="Google Books" </a> <a target="_blank" href="https://play.google.com/store/books/details/Sudharsan_Ravichandiran_Deep_Reinforcement_Learnin?id=dFkAEAAAQBAJ"> <img src="./images/googleplay_logo.png" alt="Google Play" > </a> <br> </div> <br> # Table of Contents Download the detailed and complete table of contents from [here.](https://github.com/sudharsan13296/Deep-Reinforcement-Learning-With-Python/raw/master/pdf/table%20of%20contents.pdf) ## [1. Fundamentals of Reinforcement Learning](01.%20Fundamentals%20of%20Reinforcement%20Learning) * [1.1. Key Elements of Reinforcement Learning](01.%20Fundamentals%20of%20Reinforcement%20Learning/1.01.%20Key%20Elements%20of%20Reinforcement%20Learning%20.ipynb) * [1.2. Basic Idea of Reinforcement Learning](01.%20Fundamentals%20of%20Reinforcement%20Learning/1.02.%20Basic%20Idea%20of%20Reinforcement%20Learning.ipynb) * [1.3. Reinforcement Learning Algorithm](01.%20Fundamentals%20of%20Reinforcement%20Learning/1.03.%20Reinforcement%20Learning%20Algorithm.ipynb) * [1.4. RL Agent in the Grid World](01.%20Fundamentals%20of%20Reinforcement%20Learning/1.04.%20RL%20agent%20in%20the%20Grid%20World%20.ipynb) * [1.5. How RL differs from other ML paradigms?](01.%20Fundamentals%20of%20Reinforcement%20Learning/1.05.%20How%20RL%20differs%20from%20other%20ML%20paradigms%3F.ipynb) * [1.6. Markov Decision Processes](01.%20Fundamentals%20of%20Reinforcement%20Learning/1.06.%20Markov%20Decision%20Processes.ipynb) * [1.7. Action Space, Policy, Episode and Horizon](01.%20Fundamentals%20of%20Reinforcement%20L
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