In March 2016, DeepMind's AlphaGo beat Lee Sedol 4-1 in a televised match viewed by over 200 million people. There was a global shortage in Go boards, and AlphaGo's victory is seen as a landmark moment for artificial intelligence.

Shortly after in April, OpenAI launched its Gym toolkit to help researchers develop and benchmark reinforcement learning algorithms. Reinforcement learning was a key technique used in the training of AlphaGo, along with Deep Learning.

Today, interest in artificial intelligence and machine learning are at an all-time high.

Keyword trends over time (source: Google)

We believe the opportunities in AI/ML will only keep growing from here. With this in mind, we started Coder One with the mission to empower engineers, data scientists, researchers, and hobbyists with all the tools they need to build game-changing AI.

About us

We're a global team with experience across cloud computing, esports, and game-playing AI research.

Over the past 7 months, we've launched two iterations of the AI Sports Challenge. This was an AI game competition where a total of 60+ teams across 20+ countries created the best bot to compete in a recreation of the classic Bomberman game for 10,000+ AUD in cash prizes.

Finals match at the AI Sports Challenge 2021, broadcast on Twitch.

Something we heard from participants of the challenge was:

"I've been wanting to apply reinforcement learning, and this competition seemed like a good place to do so."

Participants wanted to learn and apply novel technologies. Some wanted to live out their AlphaGo dreams.

That insight is what led us here. We're now focused on building the best place for engineers, data scientists, researchers, and hobbyists to experiment with cutting-edge techniques in the broad field of artificial intelligence and get recognized for pushing the boundaries.

Coder One is a place to experiment with cutting edge machine learning algorithms in meaningful real-world challenges

Here's our three main goals for the platform:

1. Have the most challenging and immersive environments that encourage users to explore creative solutions

After running the AI Sports Challenge twice and speaking to 60+ participants, we've got a good idea of how to do this. Our philosophy on environments:

• Entertaining and intuitive, with high ceilings.
• Quality, over quantity.
• Address real-world and research challenges (partial observability, sparse rewards, multi-agents, etc.).
• Multi-agent. No real-world system ever operates in isolation. Unlike typical data science competitions where there's a single accuracy score to optimize, it's not always clear what you need to do to solve a multi-agent environment.

2. Build the most user-friendly tools and frameworks

Start in minutes, not hours. We want to make experimenting with cutting-edge algorithms accessible to anyone who's ever had an interest in the world's most challenging problems from self-driving cars to beating StarCraft II.

Our current platform is language-agnostic. We plan to provide starter kits provided for popular languages used across both industry and research, such as Python, Java, and C++.

We've already built out the infrastructure that will help us scale from hundreds of multiplayer simulations to thousands and beyond.

3. Have year-round high-stakes competitions

We know it's mostly about learning and the satisfaction of seeing your agents come to life. But that doesn't mean it can't be about prizes and prestige as well. From our previous experiences as competition organizers, we know how to raise funding for large prize pools, bring onboard exciting sponsors, and run entertaining Twitch broadcasts to showcase your creations.

What's next?

We've just released an early access version of our first sandbox environment, Bomberland, and plan to launch our first competition in late 2021.

Please check it out and let us know what you think!