Reinforcement learning (RL) is a core area of research at Amii.
Amii researchers, including Richard S. Sutton, have made some of the most important contributions to the field, helping advance how AI interacts with the world, learns from feedback, and improves over time.
At Amii, RL isn’t just a research focus—it’s core to how we advance AI for real-world impact.
What is Reinforcement Learning?
Reinforcement learning (RL) is a type of AI that learns through experience. Instead of relying on fixed datasets, RL interacts with its environment, takes actions, and learns from feedback to improve over time. This makes it critical for solving complex problems where conditions change, data is limited, or decisions have long-term consequences
Reinforcement learning (RL) is behind many of the AI-driven tools and services you use every day.
Here are a few of the many ways RL is shaping the technology around you:
Personalized Recommendations
Streaming services (Netflix, YouTube, Spotify) and online shopping platforms (Amazon) use RL to suggest content and products based on your interactions.
Smart Assistants & Chatbots
Virtual assistants like Siri, Google Assistant, and Alexa use RL to improve voice recognition and provide more relevant responses.
Robotics & Automation
RL powers warehouse robots, robotic vacuum cleaners, and industrial automation, helping machines learn how to move, pick up objects, and complete tasks efficiently.
Healthcare & Drug Discovery
RL is helping healthcare professionals make more accurate diagnoses, personalize treatment plans, and accelerate drug discovery, leading to faster, more effective healthcare solutions.
Financial Serivices & Banking
RL detects fraud in real time, protecting you from identity theft and unauthorized charges.
What Makes RL Different?
Adapting to Changing Environments
Traditional machine learning relies on static datasets, making it difficult to handle dynamic or unpredictable conditions. RL continuously interacts with its environment, learning from feedback and adjusting in real time. This makes it ideal for real-world challenges where conditions evolve, from robotics to financial markets.
Decision-Making Beyond Classification
Most AI models focus on classification—identifying objects in images, translating text, or predicting outcomes based on fixed inputs. RL goes further, learning strategies for sequential decision-making. This is essential for applications in robotics, healthcare, and finance, where success depends on a series of well-informed actions.
Learning Without Labeled Data
Labeled datasets can be expensive and time-consuming to create. RL removes this barrier by learning through trial and error, using only a reward signal to measure success. This makes it a powerful tool for autonomous learning in complex environments where structured data isn’t readily available.
Powering Autonomous Systems
RL drives AI systems that actively explore, learn, and adapt—from self-driving cars navigating unpredictable roads to industrial robots optimizing workflows in real time. Unlike traditional models, which rely on pre-existing knowledge, RL enables AI to continuously improve through experience.
Understanding RL: Video Game Example
At its core, reinforcement learning is about figuring out the best moves by learning from experience—just like you do when you get better at a game over time.
Let’s take Breakout, a classic Atari game, as an example.
The goal of Breakout is to break down a wall of bricks by bouncing a ball against it. The ball can only be controlled by moving a paddle to deflect it back toward the bricks. Points are earned as bricks are broken.
Let’s look at how its game mechanics map to the key ideas behind RL: