TLDR: Reinforcement Learning (RL) teaches AI to improve through millions of trial and error attempts at warp speed, powering everything from back-flipping robots to smarter cities. Trust me, it’s going to be a very big deal.
Remember that rush when you finally beat that impossible video game boss after fifty attempts?
Or mastered a skateboard trick after face-planting and ripping up your knees?
That's your brain on reinforcement learning—trying, failing, adjusting, and eventually nailing it. Now imagine bottling that learning process, turbocharging it with a million virtual attempts per hour, and unleashing it on everything from robot dogs to traffic systems.
Welcome to reinforcement learning—where agents learn not by being told what to do, but by figuring it out themselves.
Learning by Doing it Millions of Times
Picture this: You're teaching your friend to ride a bike.
Option A: Spend hours explaining the physics of balance, tire friction, and momentum.
Option B: Put them on the bike, give a little push, and let them figure it out (with a helmet, obviously).
Reinforcement learning (let’s just say “RL”) is firmly in camp B—it's all about learning by doing, failing, and trying again.
But here's where computers have us beat: While you might try a skateboard trick 50 times before landing it, an AI can attempt the same task 50 million times before breakfast. And unlike humans, computers don't get tired, frustrated, or need ice packs for their bruised elbow.
"But wait, Nye" you ask, "isn't AI just about massive language models like ChatGPT spitting out essays and code?" That's just one flavor of the AI ice cream shop, friend. Reinforcement learning is the chocolate chip cookie dough that's about to take over the menu.
Here. --- Let's do the classic industry example: Imagine a digital mouse in a virtual maze.
At first, the mouse has no clue what to do—it's like you being dropped into a foreign city without Google Maps. It tries going left (smacks into a wall!), right (uh-oh, dead end!), backward (nothing useful there either!). Each time it makes a choice, it gets feedback:
Find cheese: +10 points (woohoo!)
Hit wall: -1 point (ouch)
Wander in circles: -0.1 points (boring penalty)
No human tells the mouse which way to turn. Instead, after thousands of attempts, it builds a mental map: "Turn left at the first junction, right at the second, and—jackpot!—virtual cheese awaits." This is reinforcement learning in its purest form: try, get feedback, adjust, repeat.
In a virtual world, this mouse is called an “agent.” The series of actions or decisions is makes is called the “policy.” And the reason the agent makes the decisions it makes is called the “reward function.” It’s all crazy math that is far beyond our heads, but at it’s core, it allows the agent to learn how to do things through million and millions of attempts.
What makes this revolutionary is how it mirrors human learning but at warp speed. When you learned to walk, nobody downloaded "walking.exe" into your brain. You stood up, fell down, and kept trying until neural connections formed that helped you balance. RL works the same way—but can compress years of human learning into hours.
While programming each line of code is cumbersome. We can use reinforcement learning to train autonomous systems, like, (I’m guessing) personalized robots.
RL's Greatest Hits
Remember when IBM's Deep Blue beat chess champion Garry Kasparov in 1997? That WAS NOT reinforcement learning—it was brute-force calculation. Same with Watson defeating Ken Jennings on Jeopardy!
Fast forward to 2016, when Google DeepMind's AlphaGo defeated world champion Lee Sedol at Go—a game with more possible positions than atoms in the universe. AlphaGo couldn't just calculate every move; it had to develop intuition through reinforcement learning.
RL has only grown more powerful:
Boston Dynamics' Backflipping Robots: Those viral videos of robots doing parkour? They're powered by RL algorithms that learned through millions of simulated attempts before executing a single real-world flip.
OpenAI's Dota 2 Bots: In 2019, OpenAI Five defeated world champion e-sports players at Dota 2—a complex strategy game requiring teamwork and adaptability. The bots trained by playing 180 years' worth of games... daily.
Reinforcement Learning for Characters: Nvidia is working on using RL for Physics-Based Character Animation. With adversarial reinforcement learning, physically simulated characters can be developed that automatically learn responsive behaviors in decades of years of training that take only days of human time.
The common thread?
None of these systems were explicitly programmed with solutions. They discovered strategies through trial and error—often finding approaches humans never considered. AI can learn to cook, clean, drive, write, create by practicing tasks millions of times.
The secret sauce behind this revolution? Game engines as AI gymnasiums. The tools powering your favorite games—Unity, Unreal Engine, O3DE, Godot, and others—are becoming training grounds for tomorrow's AI. Nvidia is building their own virtual engine, Isaac Sim. We'll likely create everything we need AI to do virtually, letting it practice in physics-enabled 3D environments according to properly designed reward functions.
Why build expensive real-world testing facilities when you can create perfect digital twins? Want to train a delivery robot? Drop it into a virtual neighborhood with realistic weather, traffic patterns, and the occasional surprise squirrel crossing its path.
These tools are increasingly accessible. Five years ago, building an RL system required a PhD. Today, high schoolers with gaming laptops are beginning to experiment with reinforcement learning using free tools and YouTube tutorials.
Getting started with RL
If you are already familiar with RL, feel free to shoot me a line, I am eager to learn as much as I can. If you are interested in learning RL yourself, (like I am endeavoring to), below are my suggestions to start.
Learn a game engine. Preferably several. There are a number to choose from. You can go with one of the big dogs like Unity or Unreal, or find one of the open source versions like Godot, or the Linux Foundation’s O3DE. Moving things in the virtual world will increasingly be a valuable skill.
Here’s a RL agent course that I’m playing with for Godot:
https://huggingface.coGodot RL Agents - Hugging Face Deep RL Course
We're on a journey to advance and democratize artificial intelligence through open source and open science.
Use Cases. Just because you can use RL, doesn’t mean you actually know what to do with it. We will need to figure out what to teach AI, as much as the how. That’s why I also recommend designers and creatives play with LLM driven agents. There are a number of open source platforms like Menlo.ai, that allow you to experiment with models like R1, Lllama3, or if you have the API key Claude and Open AI. You can also play with a variety of models and agents on the AI open source playground Hugging Face. You can connect with me here.
Simulation | Menlo Research
Building a (virtual) robotics R&D lab
Too hard? Keep your eyes open. Even if the above sounds too complicated, hang in there. I’ve noticed that tools for AI are only getting better and more accessible UX. When voice integration becomes common place, expect things to move even faster. If you can’t do something now, keep an eye out for the next software or open source model on the horizon.
Good luck. Let me know how you make out. Watch this space. I’ll be updating as I learn more!
Nye Warburton is an educator, game developer and creative technologist. This essay was written by recording improvisational sessions with Otter.ai, and using the output as data for a series of essay generations in Claude Sonnet 3.7. Images created with Leonardo.ai. Editing was done with human labor. For more information visit https://nyewarburton.com
