You can go broke making an AI. Did you know Google spent about $3.2 million training AlphaStar? Yeah, I don’t have that kind of budget… yet ;). But that’s part of the challenge, right? Figuring out how to build competitive AI that can go toe-to-toe with humans without burning through your savings.
I recently gave a talk on how to train gaming AI on a budget with some incredible AI creators, including Timo previously from Google’s DeepMind team. We covered a ton of ground, but I’ve distilled the most actionable insights here for you.
Key Takeaways
1. Leverage Existing Tools and Frameworks
Why reinvent the wheel?
Kaiyotech talked about how Rocket League bots thrive with Rocket League Gym, thanks to tools that let them stand on the shoulders of giants. Similarly, SC2 bots have powerful frameworks that save countless hours of development.
Takeaway: Instead of building everything from scratch, leverage existing frameworks to accelerate development and focus on innovation rather than re-creating fundamental tools.
2. Focus on Scoped Goals Over Ambitious Full-Scale Projects
AlphaStar’s full-scale approach is inspiring, but let’s be real—it’s not practical for most of us.
Timo emphasized starting small and focused. Instead of aiming to build an AI that does everything, tackle specific challenges like:
- Mastering micromanagement in an RTS
- Using reinforcement learning to refine build orders
- Creating bots that specialize in a single strategic concept
Takeaway: Think in milestones, not moonshots. Small wins stack up and lead to meaningful progress.
3. Prioritize Reward Design and Training Efficiency
Reinforcement learning is only as good as its rewards.
Peter highlighted how reward shaping makes or breaks RL training. We’ve seen with SC2 AIs that results can be mixed, but designing dense, meaningful reward objectives helps guide AI toward smarter behaviors faster.
Takeaway: Consider integrating supervised learning on replays to create a solid baseline before diving deeper into RL. Train smarter, not harder.
Final Thoughts
Building AI for gaming feels like the wild west . There aren’t many roadmaps, and it’s easy to hit dead ends. But that’s what also makes it exciting. The real win is solving the big, tough challenges, not getting bogged down by the smaller stuff—which is why leveraging the right tools and frameworks is so important. It frees up your time to focus on what really matters.
What’s your biggest challenge in building gaming AI right now? Drop a reply!