Note

:white_check_mark::100: it means, I have seen and tried them, I definitely recommend them to you

🔥 If you want to get free access to the books, courses and articles, get in touch with me with social media🔥

📣 You can help improve this repo, by giving me a ⭐star⭐ and ❤️following me❤️

❗️What is Reinforcement Learning(RL)❓️

Reinforcement learning (RL) is a field of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

You can also read below blog to understand the key concepts in Reinforcement Learning

:link: https://medium.com/intro-to-artificial-intelligence/key-concepts-in-reinforcement-learning-2af715dfbfa

:world_map:Roadmap

Warning This is only a segguestion roadmap

:books: Books

*"Reinforcement Learning: An Introduction - Second Edition - Richard S. Sutton and Andrew G. Barto"

"Deep Reinforcement Learning with Python - Second Edition - Sudharsan Ravichandiran"

"Grokking Deep Reinforcement Learning - Miguel Morales"

"PyTorch 1.x Reinforcement Learning Cookbook - Yuxi (Hayden) Liu"

"Deep Reinforcement Learning Hands-On - 2nd Edition - Maxim Lapan"

"TensorFlow 2 Reinforcement Learning Cookbook - 2nd Edition - Praveen Palanisamy"

:page_facing_up: Articles

📍Sorted by Time📍

1958 Oct: Rosenblatt, F. "The perceptron: A probabilistic model for information storage and organization in the brain.."

1983 Oct: Andrew G. Barto, et al. "Neuronlike adaptive elements that can solve difficult learning control problems."

1988 Feb (TD): Sutton, R.S. "Learning to Predict by the Methods of Temporal Differences."

1992 May: Watkins, C.J.C.H., Dayan, P. "Q-learning". Mach Learn 8, 279–292 (1992) 

1994 Nov: G. A. Rummery, M. Niranjan. "On-Line Q-Learning Using Connectionist Systems"

1995 Mar: Gerald Tesauro. "Temporal Difference Learning and TD-Gammon."

2005 Oct: Riedmiller, Martin. "Neural fitted Q iteration–first experiences with a data efficient neural reinforcement learning method." 

2012 Jul (ALE): Bellemare, Marc G., et al. "The Arcade Learning Environment: An Evaluation Platform for General Agents." 

2013 Dec (DQN): Volodymyr Mnih, et al. "Playing Atari with Deep Reinforcement Learning." 

2015 Feb (DQN): Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning."

2015 Feb: Bernhard Schölkopf. "Learning to see and act." Nature518, pages486–487 (2015)

2015 Sep (DDQN): Hado van Hasselt, et al. "Deep Reinforcement Learning with Double Q-learning." 

2015 Sep (DDPG): Lillicrap, Timothy P., et al. "Continuous control with deep reinforcement learning." 

2015 Nov: Ziyu Wang, et al. "Dueling Network Architectures for Deep Reinforcement Learning." 

2015 Nov (PER): Schaul, Tom, et al. "Prioritized Experience Replay." 

2016 Jan: David Silver, et al. "Mastering the game of Go with deep neural networks and tree search."

2016 Jun: Brockman, Greg, et al. "Openai gym." arXiv preprint arXiv:1606.01540. 

<img src="https://img.shields.io/badge/Read the Paper-green?style=for-the-badge