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The author ported a Python simulation of weakly electric fish to C and optimized it to run at one million steps per second—a 250x speedup over the original. This enabled training RL policies for the fish in under 15 seconds while maintaining biological accuracy, demonstrating how performance engineering unlocks rapid experimentation in complex simulations.
- Ported a weakly electric fish RL simulation from Python to C, boosting speed from 4,300 to 1.85M steps/sec (250x), enabling full policy training in under 10-15 seconds on a single GPU.
- Exploiting the biological detail that sensory receptors have limited range (4-10cm vs 100cm) let them skip most fish-to-fish field calculations, fixing the main bottleneck.
- Switching from double- to single-precision floats alone gave a 30% speedup.
- Shows that fast iteration—borrowed from autonomous driving RL practices—can work even in messy, physics-heavy biological simulations, and the whole thing is only 1,200 lines of open-sourced C code.
+ weakly electric fish
+ reinforcement learning
+ performance engineering
+ simulation
optimization
biology