Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, Ilya Sutskever, Evolution Strategies as a Scalable Alternative to Reinforcement Learning, arXiv:1703.03864
Felipe Petroski Such, Vashisht Madhavan, and others, Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning arXiv:1712.06567
Lehman and Stanley, Abandoning Objectives: Evolution Through the Search for Novelty Alone, Journal Evolutionary Computation archive Volume 19 Issue 2, Summer 2011 Pages 189-223
Deep Reinforcement Learning Hands-On
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Deep Reinforcement Learning Hands-On
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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.
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Current Title:
Deep Reinforcement Learning Hands-On
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
Contributors
Preface
Other Books You May Enjoy
Free Chapter
What is Reinforcement Learning?
OpenAI Gym
Deep Learning with PyTorch
The Cross-Entropy Method
Tabular Learning and the Bellman Equation
Deep Q-Networks
DQN Extensions
Stocks Trading Using RL
Policy Gradients – An Alternative
The Actor-Critic Method
Asynchronous Advantage Actor-Critic
Chatbots Training with RL
Web Navigation
Continuous Action Space
Trust Regions – TRPO, PPO, and ACKTR
Black-Box Optimization in RL
Beyond Model-Free – Imagination
AlphaGo Zero
Index
Customer Reviews