Overview

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment and receiving feedback in the form of rewards or penalties.

The goal of RL algorithms, such as Q-learning and Deep Q-Networks (DQN), is for the agent to learn a policy that maximizes cumulative reward over time. This technique has been particularly successful in game playing, robotics, and autonomous systems.

Key aspects

In 2026, reinforcement learning will be widely used in industries like healthcare and finance, where it can optimize processes or strategies based on dynamic environments. For example, RL could help manage inventory levels by predicting demand fluctuations.

Frameworks such as Google's Dopamine and OpenAI's Gym are expected to continue advancing the development of RL models, making them more accessible to developers and researchers alike.