Reinforcement Learning (RL)

TL;DR

An agent learns to make sequential decisions by trial and error, using rewards and penalties to guide learning.
RL trains agents to optimize behavior in an environment to achieve long-term goals.
Common applications include control problems, games, and natural language processing.

Definition

Reinforcement learning (RL) is a type of machine learning that involves teaching an agent to make decisions in an environment in order to maximize a reward. The agent learns through trial and error, receiving positive or negative reinforcement based on its actions and their consequences.

Explanation

In RL, an agent interacts with an environment and selects actions that affect the environment’s state. After each action the agent receives feedback in the form of rewards (positive reinforcement) or punishments (negative reinforcement). Over repeated interactions, the agent uses this feedback to improve its decision-making so as to maximize cumulative reward. RL algorithms can be applied to a variety of problems, including control problems, games, and natural language processing.

Examples

Self-driving cars

Imagine a self-driving car that is learning to navigate through a city. The car is equipped with sensors that provide it with information about its surroundings, including traffic lights, pedestrians, and other vehicles. The car’s goal is to reach its destination safely and efficiently, while following traffic rules and avoiding collisions.

In this scenario, the car’s actions can be thought of as a series of decisions that it makes in order to reach its goal. For example, when it approaches a traffic light, it must decide whether to stop, turn, or go straight. The car receives positive reinforcement (a reward) for making the right decision, such as getting to its destination safely, and negative reinforcement (a punishment) for making a wrong decision, such as causing an accident.

Using an RL algorithm, the car can learn from its experiences and improve its decision-making over time. For example, if the car consistently stops at a traffic light that is always green, it will learn to recognize this pattern and adjust its behavior accordingly. Similarly, if the car receives a reward for avoiding a collision with a pedestrian, it will learn to prioritize safety when making decisions.

Game-playing AI

RL algorithms are also commonly used to train game-playing AI. For example, consider a game of chess. The goal of the game is to capture the opponent’s king, while also protecting one’s own pieces. The chessboard can be thought of as an environment, and the pieces as agents that make decisions based on the rules of the game.

In this case, the RL algorithm would teach the AI to make strategic moves based on the current state of the game. The AI receives positive reinforcement for making good moves (such as capturing an opponent’s piece) and negative reinforcement for making bad moves (such as exposing its own pieces to capture). Over time, the AI learns to optimize its decision-making based on the rewards it receives.

Use cases

Control problems
Games
Natural language processing

Machine learning
Agent
Environment
Reward
Trial and error
Control problems
Game-playing AI
Self-driving cars
Natural language processing