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 reinforcement and enable a machine to learn by itself.
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
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, learn numerous 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.
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