marlbenchmark/on-policy

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2085 stars · 385 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

The official PyTorch implementation of Multi-Agent PPO (MAPPO), an on-policy reinforcement learning algorithm for cooperative multi-agent settings. It includes environment wrappers, training runners, and tuned hyperparameter scripts for benchmarks such as SMAC, SMACv2, Hanabi, MPEs, and Google Research Football.

Use cases

• train multi-agent reinforcement learning policies with PPO
• reproduce MAPPO benchmark results on SMAC or Hanabi
• compare on-policy vs off-policy algorithms in cooperative multi-agent games
• get a strong baseline for multi-agent RL research
• run RL experiments on StarCraftII SMAC v2 or Google Research Football
• study ablation factors that affect PPO performance in multi-agent settings

When to choose

• you need a well-tuned, paper-backed MAPPO baseline for cooperative multi-agent benchmarks
• you want reproducible training scripts with published hyperparameters and curves
• your research targets SMAC, Hanabi, MPEs, or Google Research Football environments

When to avoid

• you need off-policy multi-agent algorithms like QMIX or MADDPG
• you want a general-purpose RL library with many algorithms rather than a focused MAPPO implementation
• you lack a GPU or the specific environment dependencies (StarCraftII, Hanabi, etc.)

Facets

library · maturity active

reinforcement-learning machine-learning benchmarking reinforcement-learning machine-learning artificial-intelligence gaming-tools python mappo ppo multi-agent marl smac hanabi starcraft2 google-football pytorch research-code gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="marlbenchmark/on-policy")