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
人気のある作家
J KING (12) JJ TAM (12) yang hu (11) Al Sweigart (8) Mojang AB (8) desti publishhings (7) Hidenori Kusaka (6) John Bach (6) JP TAM (6) Andrea Vedaldi (5) Halonjash Publications (5) Hiro Ainana (5) Horst Bischof (5) Intelligent Feather Publications (5) Jan-Michael Frahm (5) Michael W. Lucas (5) Andrew Park (4) Benjamin Smith (4) Engr. Michael David (4) Harvey Deitel (4)最適なファイルサイズ
10531 KB 1079 KB 1116 KB 1233 KB 2661 KB 370 KB 484 KB 536 KB 649 KB 738 KB 790 KB 10049 KB 1006 KB 10137 KB 1016 KB 102097 KB 1029 KB 10325 KB 1032 KB 1035 KB