Reinforcement Learning: Industrial Applications of Intelligent Agents

Reinforcement Learning: Industrial Applications of Intelligent Agents
ePUB
  • eBook:
    Reinforcement Learning: Industrial Applications of Intelligent Agents
  • Author:
    Phil Winder Ph. D.
  • Edition:
    1 edition
  • Categories:
  • Data:
    December 1, 2020
  • ISBN:
    1098114833
  • ISBN-13:
    9781098114831
  • Language:
    English
  • Pages:
    408 pages
  • Format:
    ePUB

Book Description
Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcementand enable a machine to learn by itself.
Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learnnumerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML.
  • Learn what RL is and how the algorithms help solve problems
  • Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning
  • Dive deep into a range of value and policy gradient methods
  • Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning
  • Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more
  • Get practical examples through the accompanying website

Content

1. Why Reinforcement Learning?
2. Markov Decision Processes, Dynamic Programming, and Monte Carlo Methods
3. Temporal-Difference Learning, Q-Learning, and n-Step Algorithms
4. Deep Q-Networks
5. Policy Gradient Methods
6. Beyond Policy Gradients
7. Learning All Possible Policies with Entropy Methods
8. Improving How an Agent Learns
9. Practical Reinforcement Learning
10. Operational Reinforcement Learning
11. Conclusions and the Future

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