Skip to main content Accessibility help
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

1 results

  • 12 - Large State Spaces: Value Function Approximation

  • Shie Mannor, Technion - Israel Institute of Technology, Haifa, Yishay Mansour, Tel-Aviv University, Aviv Tamar, Technion - Israel Institute of Technology, Haifa
  • Book:
    Reinforcement Learning Foundations
    Published online:
    31 July 2026
    Print publication:
    20 August 2026, pp 154-181

  • Summary

    This chapter addresses the curse of dimensionality through value function approximation. Parameterized function approximators represent value functions compactly and generalize across states. The chapter frames approximate policy evaluation as supervised learning. Key theoretical objects include the projection operator and projected Bellman equation. Least squares temporal difference (LSTD) provides a batch solution for linear approximation. Deep Q-networks combining neural networks with experience replay are introduced as the foundation for deep RL successes.