Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Sách Reinforcement Learning for Sequential Decision and Optimal Control (sách keo gáy, bìa mềm)
Thể loại:Computers - Artificial Intelligence (AI)
Năm:2023
Ngôn ngữ:english
Trang:484
Have you ever wondered how AlphaZero learns to defeat the top human Go
players? Do you have any clues about how an autonomous driving system
can gradually develop self-driving skills beyond normal drivers? What is
the key that enables AlphaStar to make decisions in Starcraft, a
notoriously difficult strategy game that has partial information and
complex rules? The core mechanism underlying those recent technical
breakthroughs is reinforcement learning (RL), a theory that can help an
agent to develop the self-evolution ability through continuing
environment interactions. In the past few years, the AI community has
witnessed phenomenal success of reinforcement learning in various
fields, including chess games, computer games and robotic control. RL is
also considered to be a promising and powerful tool to create general
artificial intelligence in the future. As an interdisciplinary field of
trial-and-error learning and optimal control, RL resembles how humans
reinforce their intelligence by interacting with the environment and
provides a principled solution for sequential decision making and
optimal control in large-scale and complex problems. Since RL contains a
wide range of new concepts and theories, scholars may be plagued by a
number of questions: What is the inherent mechanism of reinforcement
learning? What is the internal connection between RL and optimal
control? How has RL evolved in the past few decades, and what are the
milestones? How do we choose and implement practical and effective RL
algorithms for real-world scenarios? What are the key challenges that RL
faces today, and how can we solve them? What is the current trend of RL
research? You can find answers to all those questions in this book. The
purpose of the book is to help researchers and practitioners take a
comprehensive view of RL and understand the in-depth connection between
RL and optimal control. The book includes not only systematic and
thorough explanations of theoretical basics but also methodical guidance
of practical algorithm implementations. The book intends to provide a
comprehensive coverage of both classic theories and recent achievements,
and the content is carefully and logically organized, including basic
topics such as the main concepts and terminologies of RL, Markov
decision process (MDP), Bellman’s optimality condition, Monte Carlo
learning, temporal difference learning, stochastic dynamic programming,
function approximation, policy gradient methods, approximate dynamic
programming, and deep RL, as well as the latest advances in action and
state constraints, safety guarantee, reference harmonization, robust RL,
partially observable MDP, multiagent RL, inverse RL, offline RL, and so
on.
