Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
Other Books You May Enjoy


  1. Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver, 2017, Rainbow: Combining Improvements in Deep Reinforcement Learning. arXiv:1710.02298

  2. Sutton, R.S. 1988, Learning to Predict by the Methods of Temporal Differences, Machine Learning 3(1):9-44

  3. Hado Van Hasselt, Arthur Guez, David Silver, 2015, Deep Reinforcement Learning with Double Q-Learning. arXiv:1509.06461v3

  4. Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Pilot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg, 2017, Noisy Networks for Exploration arXiv:1706.10295v1

  5. Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaus, David Saxton, Remi Munos 2016, Unifying Count-Based Exploration and Intrinsic Motivation arXiv:1606.01868v2

  6. Jarryd Martin, Suraj Narayanan Sasikumar, Tom Everitt, Marcus Hutter, 2017, Count-Based Exploration in Feature Space...