This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential de
In addition to introducing these new methods, this thesis presents extensive empirical results in multiple domains demonstrating that these techniques can subst
Much of the focus on finding good representations in reinforcement learning has been on learning complex non-linear predictors of value. Methods like policy gra
Markov decision processes have become the de facto standard in modeling and solving sequential decision making problems under uncertainty. This book studies lif
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding
