Reinforcement Learning and Optimal Control

Dimitri Bertsekas

评分 0.0分

This book considers large and challenging multistage decision problems, which can be solved in principle by dynamic programming, but their exact solution is computationally intractable. It can be used

Introduction to Probability (2/e)

Dimitri P. Bertsekas, John N. Tsitsiklis

评分 8.8分

An intuitive, yet precise introduction to probability theory, stochastic processes, and probabilistic models used in science, engineering, economics, and related fields. The 2nd edition is a substanti

Nonlinear Programming

Dimitri P. Bertsekas

评分 9.0分

This extensive rigorous texbook, developed through instruction at MIT, focuses on nonlinear and other types of optimization: iterative algorithms for constrained and unconstrained optimization, Lagran

Introduction to Linear Optimization

Dimitris Bertsimas, John N. Tsitsiklis

评分 9.1分

This book provides a unified, insightful, and modern treatment of linear optimization, that is, linear programming, network flow problems, and discrete optimization. It includes classical topics as we

Neuro-Dynamic Programming

Dimitri P. Bertsekas, John N. Tsitsiklis, John Tsitsiklis

评分 0.0分

This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dyna

Dynamic Programming and Optimal Control (2 Vol Set)

Dimitri P. Bertsekas

评分 9.8分