Solving complex combinatorial challenges with Reinforcement Learning: the PCB routing demo

Designing a printed circuit board (PCB) is a classic example of a complex combinatorial problem: hundreds of components need to be connected without overlaps, while minimizing path lengths and respecting physical constraints. The number of possible configurations quickly becomes astronomical, making traditional approaches inefficient.

In this 15-minute demo, InstaDeep will showcase how Reinforcement Learning (RL), combined with their MCP framework, can tackle PCB routing by learning to “play the game” of placing and connecting components. Beyond PCB design, this approach illustrates how RL can be applied to real-world optimization problems in industries such as logistics, infrastructure, and even biology.