| --- |
| license: mit |
| tags: |
| - multi-agent-rl |
| - mappo |
| - reward-attribution |
| - representation-geometry |
| - smacv2 |
| - tribal-village |
| --- |
| |
| # rl-workshop-2026 — Final Policy Checkpoints |
|
|
| Final MAPPO policy checkpoints for the workshop paper *"Feedback Attribution |
| Determines Representation Geometry in Multi-Agent RL."* Logged alongside W&B |
| project [`tashapais/rl_workshop_2026`](https://wandb.ai/tashapais/rl_workshop_2026). |
|
|
| Each `.pt` is a dict with key `"model"` (PyTorch `state_dict` for the |
| `ActorCritic` defined in the `tribal-village` repo's `experiments/`). |
|
|
| ## Table 1 — Tribal Village (12 agents, 308 actions), 4M agent-steps |
| `tribal_village/<run>/step_4002816.pt`, reward attribution |
| `r_i^alpha = (1-alpha) r_i + alpha * mean_j r_j`: |
|
|
| | Condition | alpha | seeds | |
| |-----------|-------|-------| |
| | Individual | 0.0 | 0,1,2 | |
| | Mixed | 0.8 | 0,1,2 | |
| | Shared | 1.0 | 0,1,2 | |
|
|
| ## Table 2 — SMACv2 10gen_terran (6 terran units), 2M steps |
| `smacv2/<run>/step_2001408.pt`: |
|
|
| | Condition | seeds | |
| |-----------|-------| |
| | Individual (per-agent reward) | 0,1,2 | |
| | Shared (team-averaged) | 0,1,2 | |
|
|
| ## Caveats (see repo `runs.md`) |
| - Tribal Village runs **fail the behavior gate** (no-op/random baseline) at 4M |
| steps under passive shaping; representation geometry from them is direction- |
| consistent (probe declines 0.75→0.50) but not yet behavior-grounded. |
| - SMAC `individual` agents are weak (~1.7% win) vs `shared` (~25%); SMAC D_act |
| is mask-contaminated and not paper-quotable without a mask-aware recompute. |
| |
