Machine Learning & Artificial Intelligence · Artificial Intelligence & Deep Learning

Reinforcement Learning and Markov Decision Processes

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction // Stochastic Optimal Control · Artificial Intelligence

Overview

Topics covered

  • Introduction
  • Introduction and the Reinforcement Learning Paradigm
  • Markov processes
  • Stationary Markov Processes
  • Stationary Distributions
  • Mathematical Structure of the MDP

Related concepts

Main connections

  • Monte Carlo simulation
  • Variance & standard deviation
  • Calculus & differentiation
  • Mathematical expectation & moments
  • Linear algebra & matrices
  • Neural networks & deep learning

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

τ=(S0,A0,R1,S1,A1,R2,…,St−1,At−1,Rt,St)
View LaTeX source\tau = (S_0, A_0, R_1, S_1, A_1, R_2, \dots, S_{t-1}, A_{t-1}, R_t, S_t)