Once upon a time, machine learning was an arcane field, the preserve of a precious few researchers holed up in grand academic institutions. Progress was slow, and hard won. Today, however, just about anyone with a computer can dive into these topics and develop their own machine learning systems.
Shawn Hymel has been doing just that, in his work in developer relations and as a broader electronics educator. His current interest is reinforcement learning on a tiny scale. He came down to the 2023 Hackaday Supercon to tell us all about his work.
Rewards Are Everything
Shawn finds reinforcement learning highly exciting, particularly when it comes to robotics. “We’re now getting into the idea of, can robots not just do a thing you tell them to, but can they learn to do the thing you tell them to?” he says. Imagine a robot copter that learns to fly itself, or a self-driving car that intuitively learns to avoid pedestrians. The dream is robots that do not simply blindly follow orders, but learn and understand intuitively what to do.
The reinforcement learning system for controlling an inverted pendulum.
Obviously, a great deal of machine learning research involves teams of PhDs and millions of dollars in funding. As an individual, Shawn decided to start smaller. Rather than try and build an advanced quadripedal robot that could teach itself to walk, he instead started with a simple inverted pendulum. It’s a classical control theory project, but he set about getting it to work with reinforcement learning instead.
Reinforcement learning is all about observat ..
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