Fundamentals of Reinforcement Learning

Fundamentals of Reinforcement Learning

AI, But Simple Issue #9

Reinforcement Learning (RL) is a subset of machine learning that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences.

• The fundamental idea behind RL is to allow computers to learn from their mistakes.

• This is very similar to human behavior, as we evolved through trial and error.

Some common applications of RL include playing games (AlphaGo, chess), robotics (BostonDynamics robots), autonomous vehicles (self-driving cars), finance-related activities (algorithmic trading), and other optimization problems.

In RL, the agent is the decision-maker who interacts with the environment to achieve a certain goal.

Unlike supervised learning, where the feedback provided to the agent (or model) are correct labels, reinforcement learning uses rewards and punishments as signals for positive and negative behavior.

• In RL, there is no direct indication of the correct action, but just a positive or negative signal

In reinforcement learning, the agent’s objective is to learn a policy (defines the agent’s behavior) to maximize the amount of reward

• A policy can be thought of as a strategy to determine the actions an agent will take at a specific period in time (state)

Some key terms that describe the basic elements of an RL problem are:

1. Environment — Physical world in which the agent operates

2. State — Current situation of the agent

3. Reward — Feedback from the environment

4. Policy — Method to map agent’s state to actions

5. Value — Future reward that an agent would receive by taking an action in a particular state

Let’s use a game example to model an RL problem. Let’s suppose we were playing the game Pac-Man, where the goal of the game is to eat the little circles without losing all of your lives.