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version: "1.0.0" name: vmas-simulator-guide description: "Vectorized multi-agent reinforcement learning simulator" metadata: openclaw: emoji: "🎮" category: "domains" subcategory: "ai-ml" keywords: ["multi-agent RL", "VMAS", "simulator", "reinforcement learning", "vectorized", "cooperative"] source: "https://github.com/proroklab/VectorizedMultiAgentSimulator"

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	Cooperative	3	Explore and discover
navigation	Mixed	N	Multi-agent navigation

Integration with MARL Libraries

python

# With TorchRL
from torchrl.envs import VmasEnv
env = VmasEnv(
    scenario="simple_spread",
    num_envs=512,
    device="cuda",
)
# With RLlib
from ray.rllib.env import MultiAgentEnv
# VMAS provides RLlib-compatible wrapper
# With CleanRL / custom training
import torch
env = vmas.make_env("transport", num_envs=2048, device="cuda")
obs = env.reset()
# All tensors on GPU — train directly without CPU transfer
policy_output = policy_network(obs[0])  # Agent 0 observations

Custom Scenarios

python

from vmas import Scenario, Agent, World, Landmark
class MyScenario(Scenario):
    def make_world(self, batch_dim, device):
        world = World(batch_dim=batch_dim, device=device)
        world.add_agent(Agent(name="agent_0"))
        world.add_agent(Agent(name="agent_1"))
        world.add_landmark(Landmark(name="goal"))
        return world
    def reset_world(self, env, world):
        # Randomize positions
        for agent in world.agents:
            agent.set_pos(torch.rand(env.batch_dim, 2) * 2 - 1)
    def reward(self, agent, world):
        # Distance to goal
        goal = world.landmarks[0]
        return -torch.linalg.norm(agent.state.pos - goal.state.pos,
                                   dim=-1)
# Register and use
env = vmas.make_env(MyScenario(), num_envs=512)

Use Cases

MARL research: Benchmark multi-agent algorithms
Cooperative learning: Study emergent coordination
Scalability testing: GPU-accelerated parallel training
Custom scenarios: Design domain-specific multi-agent tasks
Education: Teach multi-agent RL concepts

References

All versions