Tasha02/epymarl-mappo-smacv2-protoss-5-vs-5

MAPPO on SMACv2 protoss_5_vs_5

Multi-Agent PPO (MAPPO) agents trained with EPyMARL on the SMACv2 protoss_5_vs_5 scenario (StarCraft II).

• Algorithm: MAPPO (shared parameters, RNN policy, centralised value function)
• Environment: SMACv2 protoss_5_vs_5
• Checkpoint step: 10051667 environment timesteps
• Greedy test win rate at upload: ~0.50 greedy (converged, 10M steps; peak 0.54-0.65)

Files

• agent.th — actor network weights
• critic.th — centralised critic weights
• *_opt.th — optimiser states
• config.json — full training configuration

Usage

Load into EPyMARL by pointing checkpoint_path at a directory containing a 10051667/ subfolder with these files:

python src/main.py --config=mappo --env-config=sc2v2 \
    with env_args.map_name=protoss_5_vs_5 checkpoint_path=<dir> evaluate=True render=False