coac/ml-agents — explained in plain English
Analysis updated 2026-07-18 · repo last pushed 2018-05-15
Train an NPC to play a game more intelligently through reinforcement learning.
Study how simulated agents or robot swarms learn to coordinate.
Use the example environments, like the balancing-ball demo, to learn the training workflow.
| coac/ml-agents | anulman/docx-sax | atrblizzard/vtmb-sbox-mounter | |
|---|---|---|---|
| Stars | — | 0 | 0 |
| Language | C# | C# | C# |
| Last pushed | 2018-05-15 | — | — |
| Maintenance | Dormant | — | — |
| Setup difficulty | hard | moderate | moderate |
| Complexity | 4/5 | 4/5 | 3/5 |
| Audience | researcher | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires Unity plus a Python ML environment, project is still in beta with some experimental features.
Unity ML-Agents lets you train AI characters inside Unity games and simulations by letting them learn through trial and error rather than hand-coding behavior.
Mainly C#. The stack also includes C#, Unity, Python.
Dormant — no commits in 2+ years (last push 2018-05-15).
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
Verify against the repo before relying on details.