---
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.