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https://github.com/NousResearch/atropos.git
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| .. | ||
| AI_Diplomacy@70d4ae2fe0 | ||
| __init__.py | ||
| atropos_client_minimal.py | ||
| diplomacy_env_minimal.py | ||
| diplomacy_local_server.py | ||
| queue_manager.py | ||
| README.md | ||
| requirements.txt | ||
Minimal Diplomacy Environment
A simplified Diplomacy RL training environment for Atropos that integrates with AI_Diplomacy.
Overview
This minimal implementation provides:
- Basic game integration via AI_Diplomacy submodule
- Parallel rollouts with configurable group_size
- LLM request interception through AtroposClient proxy
- Simple supply center based scoring
- No complex features (no GRPO, memory systems, or advanced scoring)
Architecture
Atropos Policy Server
↓
AtroposClientMinimal (proxy)
↓
AI_Diplomacy Game Engine
↓
Game Execution
Quick Start
- Install dependencies:
pip install -r requirements.txt
cd AI_Diplomacy
pip install -e .
-
Start your Atropos policy server on port 8000
-
Run the environment:
python diplomacy_env_minimal.py serve
Configuration
Key settings in DiplomacyEnvMinimalConfig:
max_game_turns: Number of game turns (default: 10)training_power: Which power the RL agent controls (default: "FRANCE")group_size: Number of parallel games per trajectory (default: 4)
How It Works
- Parallel Rollouts: Each training step runs
group_sizegames with the same initial seed - LLM Interception: AtroposClientMinimal intercepts all LLM calls from AI_Diplomacy
- Trajectory Collection: Game interactions are collected and scored
- Best Selection: The highest scoring trajectory is returned for training