Next ForgeOpenTech
Books

Table of contents

  • 00_Overview_of_this_Book
  • 01_Basic_Concepts
  • 02_State_Values_and_Bellman_Equation
  • 03_Optimal_State_Values_and_Bellman_Optimality_Equation
  • 04_Value_Iteration_and_Policy_Iteration
  • 05_Monte_Carlo_Methods
  • 06_Stochastic_Approximation
  • 07_Temporal-Difference_Methods
  • 08_Value_Function_Methods
  • 09_Policy_Gradient_Methods
  • 10_Actor-Critic_Methods
  • 11_A_Preliminaries_for_Probability_Theory
  • 12_B_Measure-Theoretic_Probability_Theory
  • 13_C_Convergence_of_Sequences
  • 14_D_Preliminaries_for_Gradient_Descent
  • 15_Bibliography
  • 16_Symbols
  • 17_Index

README

Convergence of Sequences

We next introduce some results about the convergence of deterministic and stochastic sequences. These results are useful for analyzing the convergence of reinforcement learning algorithms such as those in Chapters 6 and 7.

We first consider deterministic sequences and then stochastic sequences.

Previous13_C_Convergence_of_Sequences
NextC.1_Convergence_of_deterministic_sequences

OpenTech

AI-Powered Reading and Learning Platform

Built withLogoNexty.dev

Languages

  • English
  • 中文
  • 日本語

Open Source

  • Next Forge
  • Landing Page Boilerplate
  • Blog Boilerplate

Other Products

  • Nexty - SaaS Template
  • OG Image Generator
  • Dofollow.Tools

Subscribe to our newsletter

Get the latest news and updates from Next Forge

Copyright © 2025 Next Forge All rights reserved.

Privacy PolicyTerms of Service
Featured on Dofollow.Tools