Reinforcement Learning: An Introduction (adaptive Computation And Machine Learning)
by Richard S. Sutton /
1998 / English / PDF
2.3 MB Download
Reinforcement learning, one of the most active research areas in
artificial intelligence, is a computational approach to learning
whereby an agent tries to maximize the total amount of reward it
receives when interacting with a complex, uncertain environment.
In Reinforcement Learning, Richard Sutton and Andrew Barto
provide a clear and simple account of the key ideas and
algorithms of reinforcement learning. Their discussion ranges
from the history of the field's intellectual foundations to the
most recent developments and applications. The only necessary
mathematical background is familiarity with elementary concepts
of probability.The book is divided into three parts. Part I
defines the reinforcement learning problem in terms of Markov
decision processes. Part II provides basic solution methods:
dynamic programming, Monte Carlo methods, and temporal-difference
learning. Part III presents a unified view of the solution
methods and incorporates artificial neural networks, eligibility
traces, and planning; the two final chapters present case studies
and consider the future of reinforcement learning.
Reinforcement learning, one of the most active research areas in
artificial intelligence, is a computational approach to learning
whereby an agent tries to maximize the total amount of reward it
receives when interacting with a complex, uncertain environment.
In Reinforcement Learning, Richard Sutton and Andrew Barto
provide a clear and simple account of the key ideas and
algorithms of reinforcement learning. Their discussion ranges
from the history of the field's intellectual foundations to the
most recent developments and applications. The only necessary
mathematical background is familiarity with elementary concepts
of probability.The book is divided into three parts. Part I
defines the reinforcement learning problem in terms of Markov
decision processes. Part II provides basic solution methods:
dynamic programming, Monte Carlo methods, and temporal-difference
learning. Part III presents a unified view of the solution
methods and incorporates artificial neural networks, eligibility
traces, and planning; the two final chapters present case studies
and consider the future of reinforcement learning.
