Design and Analysis of Efficient Reinforcement Learning Algorithms

Released on by Claude-Nicolas Fiechter
preview-18

  • Design and Analysis of Efficient Reinforcement Learning Algorithms Book Detail

  • Author : Claude-Nicolas Fiechter
  • Release Date : 1997
  • Publisher :
  • Genre : Reinforcement learning
  • Pages : 125
  • ISBN 13 :
  • File Size : 68,68 MB
DOWNLOAD BOOK

Design and Analysis of Efficient Reinforcement Learning Algorithms by Claude-Nicolas Fiechter PDF Summary

Book Description: Reinforcement learning considers the problem of learning a task or behavior by interacting with one's environment. The learning agent is not explicitly told how the task is to be achieved and has to learn by trial-and-error, using only the rewards and punishments that it receives in response to the actions it takes. In the last ten years there has been a rapidly growing interest in reinforcement learning techniques as a base for intelligent control architectures. Many methods have been proposed and a number of very successful applications have been developed. This dissertation contributes to a theoretical foundation for the study of reinforcement learning by applying some of the methods and tools of computational learning theory to the problem. We propose a formal model of efficient reinforcement learning based on Valiant's Probably Approximately Correct (PAC) learning framework, and use it to design reinforcement learning algorithms and to analyze their performance. We describe the first polynomial-time PAC algorithm for the general finite-state reinforcement learning problem and show that an active and directed exploration of its environment by the learning agent is necessary and sufficient to obtain efficient learning for that problem. We consider the trade-off between exploration and exploitation in reinforcement learning algorithms and show how in general an off-line PAC algorithm can be converted into an on-line algorithm that efficiently balances exploration and exploitation. We also consider the problem of generalization in reinforcement learning and show how in some cases the underlying structure of the environment can be exploited to achieve faster learning. We describe a PAC algorithm for the associative reinforcement learning problem that uses a form of decision lists to represent the policies in a compact way and generalize across different inputs. In addition, we describe a PAC algorithm for a special case of reinforcement learning where the environment can be modeled by a linear system. This particular reinforcement learning problem corresponds to the so-called linear quadratic regulator which is extensively studied and used in automatic and adaptive control.

Disclaimer: www.yourbookbest.com does not own Design and Analysis of Efficient Reinforcement Learning Algorithms books pdf, neither created or scanned. We just provide the link that is already available on the internet, public domain and in Google Drive. If any way it violates the law or has any issues, then kindly mail us via contact us page to request the removal of the link.

Algorithms for Reinforcement Learning

Algorithms for Reinforcement Learning

File Size : 30,30 MB
Total View : 6159 Views
DOWNLOAD

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a lon

Data Efficient Reinforcement Learning

Data Efficient Reinforcement Learning

File Size : 48,48 MB
Total View : 8783 Views
DOWNLOAD

Reinforcement learning (RL) has recently emerged as a generic yet powerful solution for learning complex decision-making policies, providing the key foundationa