— web clipping, 2,610 words — published 2026-08-21
Training artificial electric fish in under a minute
 I spent the last three years working on high-performance simulation and self-play reinforcement learning (RL) for autonomous driving. A recurring theme in my work (and that of many others) is that fast simulation and simple on-policy RL are remarkably effective for training controllable policies. In general, you need three things: 1. **A clearly specified objective:** you need to be […]
Summary by Claude Sonnet 5.5
An X article by @daphnesolves, "Training artificial electric fish in under a minute", published 2026-08-21 and clipped 2026-08-25. The filename is a clipper artefact: the source URL and author field are @daphnesolves, not SkyeSharkie. The author, who says they spent three years on fast simulation and self-play RL for autonomous driving, ports an open-source multi-agent RL model of weakly electric fish social behaviour (Singh, Yu and co-authors, arXiv 2511.08436; reported at six to nine hours per five-million-step run) from Python to C inside PufferLib, working with Joseph Suarez. The weight sits in "Making the fish simulation fast", where profiling shows electric-field computation dominates and limiting each sensory organ type to its own range (about 4-10 cm versus about 100 cm) cuts the cost sharply, and in the benchmark on one RTX 4090: about 4,300 to 1.85 million simulation steps per second, a strong policy in about 10 seconds, a 527-run sweep in 12 hours. A policy transferred back to the Python environment reaches about 93% of maximum return, though it pulses every step where the paper's fish do so sparsely, which the author reads as a different learned solution. Predictive value for experiments is left untested. Link to this archive is loose: Nathan's RL and neural-simulation work, nothing on welfare or alignment.
Full text not reproduced here — kept as Post by @SkyeSharkie on X 1.md in Nathan's clippings archive.