Deep Reinforcement Learning Fundamentals, Research and Applications / edited by Hao Dong, Zihan Ding, Shanghang Zhang.

Deep Reinforcement Learning Fundamentals, Research and Applications /

Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Dong, Hao (Editor), Ding, Zihan (Editor), Zhang, Shanghang (Editor)
Format: Electronic eBook
Language:English
Published: Singapore : Springer Singapore : Imprint: Springer, 2020.
Edition:1st ed. 2020.
Subjects:
Machine learning.
Data mining.
Optical data processing.
Robotics.
Computer programming.
Natural language processing (Computer science)
Online Access: Full text (Wentworth users only)
Table of Contents:
  • Preface
  • Contributors
  • Acknowledgements
  • Mathematical Notation
  • Acronyms
  • Introduction
  • Part 1: Foundamentals
  • Chapter 1: Introduction to Deep Learning
  • Chapter 2: Introduction to Reinforcement Learning
  • Chapter 3: Taxonomy of Reinforcement Learning Algorithms
  • Chapter 4: Deep Q-Networks
  • Chapter 5: Policy Gradient
  • Chapter 6: Combine Deep Q-Networks with Actor-Critic
  • Part II: Research
  • Chapter 7: Challenges of Reinforcement Learning
  • Chapter 8: Imitation Learning
  • Chapter 9: Integrating Learning and Planning
  • Chapter 10: Hierarchical Reinforcement Learning
  • Chapter 11: Multi-Agent Reinforcement Learning
  • Chapter 12: Parallel Computing
  • Part III: Applications
  • Chapter 13: Learning to Run
  • Chapter 14: Robust Image Enhancement
  • Chapter 15: AlphaZero
  • Chapter 16: Robot Learning in Simulation
  • Chapter 17: Arena Platform for Multi-Agent Reinforcement Learning
  • Chapter 18: Tricks of Implementation
  • Part IV: Summary
  • Chapter 19: Algorithm Table
  • Chapter 20: Algorithm Cheatsheet.

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