Reinforcement Learning Introduction

An introduction to reinforcement learning problems and solutions

This post will be an introductory level on reinforcement learning. Throughout this post, the problem definitions and some most popular solutions will be discussed. After this article, you should be able to understand what is reinforcement learning, and how to find the optimal policy for the problem.

The Problem Description

The agent-environment interaction in reinforcement learning

The Setting

• The reinforcement learning (RL) framework is characterized by an agent learning to interact with its environment.
• At each time step, the agent receives the environment’s state (the environment presents a situation to the agent), and the agent must choose an appropriate action in response. One time step later, the agent receives a reward (the environment indicates whether the agent has responded appropriately to the state) and a new state.
• All agents have the goal to maximize the expected cumulative reward.

Episodic vs. Continuing Tasks

• Continuing tasks are tasks that continue forever, without end.
• Episodic tasks are tasks with a well-defined starting and ending point.
  * In this case, we refer to a…