Architecture
Distributed trainer/worker topology, checkpoint sync, stale rollout rejection, failure handling, scaling points, and hands-off self-healing cloud operation.
Read architecture docStart here for the architecture, experiments, technical writeup, demo assets, portfolio framing, and public dashboard behind AscensionAI.
Distributed trainer/worker topology, checkpoint sync, stale rollout rejection, failure handling, scaling points, and hands-off self-healing cloud operation.
Read architecture docThe June pivot to the sts_lightspeed C++ simulator, the variant series that proved combat execution was the universal wall, and the search-combat + diagnosed-macro stack that produced the first winning agents (mainline_v3).
Running the worker/trainer stack headless on a GPU-less GCP spot VM: one-shot installer, Xvfb + software GL, Java 8, spot preemption recovery, and the nine bring-up challenges with fixes.
Read deployment notesImplementation detail on observations, actions, reward shaping, behavior cloning, PPO, parallel training, and limitations.
Read technical writeupThe full arc, from BC baseline and live-game PPO through the simulator variant series to the first winning agents (009–011), backed by the public experiment registry.
Open experimentsPlain-English guide to each training, evaluation, environment, plotting, and logging script.
Read script guideStatic results viewer with embedded public metrics and support for loading local CSV files.
Open dashboardRecruiter-facing summary, public demo assets, and resume-ready bullets.
Open portfolio page