Introduction to Deep Q Learning

June 27, 2019

Download Full Report: here 

 

Introduction

 

This document is a brief introduction to the deep Q learning (DQN) and some of its most important improvements. It also covers topics including learning from examples, multi-agent environment and using DQN to solve natural language processing problems. The last part of this document introduces the possible applications of DQN and its variants in trading.

 

Deep reinforcement learning and DQN

 

Markov Decision Process (MDP)

Markov decision process is a stochastic control process and it is usually represented by 5-tuple 

 

 

 

Markov decision process, n.d.:​

  • S is the state set

  • A is the action set

Is the transition probability (given current state and action, what’s the probability distribution of the next state)