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. 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. 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
