Amazon cover image
Image from Amazon.com

Deep reinforcement learning / Aske Plaat

By: Material type: TextTextPublication details: Singapore : Springer, c2022Description: xv, 406 pages : color illustrations ; 24 cmISBN:
  • 9789811906374
Subject(s): LOC classification:
  • Q 325.6 .P53 2022
Contents:
1. Introduction -- 2. Tabular Value-Based Methods -- 3. Approximating the Value Function -- 4. Policy-Based Methods -- 5. Model-Based Methods -- 6. Two-Agent Reinforcement Learning -- 7. Multi-Agent Reinforcement Learning -- 8. Hierarchical Reinforcement Learning -- 9. Meta Learning -- 10. Further Developments -- A. Deep Reinforcement Learning Suites -- B. Deep Learning -- C. Mathematical Background.
Summary: Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the worlds leading players. Deep reinforcement learning takes its inspiration from the fields of biology and psychology.
Item type: Books
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Home library Collection Call number Copy number Status Date due Barcode
Books Books National University - Manila LRC - Main General Circulation Machine Learning GC Q 325.6 .P53 2022 (Browse shelf(Opens below)) c.1 Available NULIB000019546

Includes index.

1. Introduction -- 2. Tabular Value-Based Methods -- 3. Approximating the Value Function -- 4. Policy-Based Methods -- 5. Model-Based Methods -- 6. Two-Agent Reinforcement Learning -- 7. Multi-Agent Reinforcement Learning -- 8. Hierarchical Reinforcement Learning -- 9. Meta Learning -- 10. Further Developments -- A. Deep Reinforcement Learning Suites -- B. Deep Learning -- C. Mathematical Background.

Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the worlds leading players. Deep reinforcement learning takes its inspiration from the fields of biology and psychology.

There are no comments on this title.

to post a comment.