vmas-simulator-guide

# VMAS: Vectorized Multi-Agent Simulator Guide ## Overview VMAS is a vectorized simulator for multi-agent reinforcement learning (MARL) that runs thousands of parallel environments on GPU via PyTorch. It provides a diverse set of 2D cooperative, competitive, and mixed scenarios for benchmarking multi-agent algorithms. Orders of magnitude faster than CPU-based simulators, enabling rapid research iteration on multi-agent coordination problems. ## Installation ```bash pip install vmas ``` ## Quick Start ```python import vmas # Create vectorized environment env = vmas.make_env( scenario="simple_spread", num_envs=1024, # Parallel environments num_agents=3, device="cuda", # GPU acceleration continuous_actions=True, ) # Environment loop obs = env.reset() for step in range(100): # Random actions for demonstration actions = [env.action_space[i].sample() for i in range(env.n_agents)] obs, rewards, dones, infos = env.step(actions) # obs: list of [num_envs, obs_dim] tensors # rewards: list of [num_envs] tensors ``` ## Scenarios | Scenario | Type | Agents | Description | |----------|------|--------|-------------| | **simple_spread** | Cooperative | 3 | Cover N landmarks | | **simple_tag** | Competitive | 4 | Predator-prey | | **transport** | Cooperative | 4 | Move package to goal | | **wheel** | Cooperative | 4 | Coordination on wheel | | **flocking** | Cooperative | 5+ | Reynolds flocking | | **discovery** ...