
Algorithms for Reinforcement Learning
Csaba Szepesvári
Algorithms for Reinforcement Learning
Csaba Szepesvári
Book Details:
Year: | 2009 |
Publisher: | Morgan & Claypool Publishers |
Pages: | 98 pages |
Language: | english |
Since: | 15/12/2015 |
Size: | 1.59 MB |
License: | Pending review |
Content:
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner’s predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms’ merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas together with a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.
Categories:
Tags:
Loading comments...
Scanning lists...
The book in numbers
online since
15/12/2015rate score
Nothing yet...votes
Nothing yet...Social likes
Nothing yet...Views
Downloads
Interest
Countries segmentation
Source Referers
Websites segmentation
evolution
Loading...