VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning

Abstract

While many multi-robot coordination problems can be solved optimally by exact algorithms, solutions are often not scalable in the number of robots. Multi-Agent Reinforcement Learning (MARL) is gaining increasing attention in the robotics community as a promising solution to tackle such problems. Nevertheless, we still lack the tools that allow us to quickly and efficiently find solutions to large-scale collective learning tasks. In this work, we introduce the Vectorized Multi-Agent Simulator (VMAS). VMAS is an open-source framework designed for efficient MARL benchmarking. It comprises a vectorized 2D physics engine written in PyTorch and a set of twelve challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface. We demonstrate how vectorization enables parallel simulation on accelerated hardware without added complexity. When comparing VMAS to OpenAI MPE, we show how MPE’s execution time increases linearly in the number of simulations while VMAS is able to execute 30,000 parallel simulations in under 10s, proving more than 100x faster. Using VMAS’s RLlib interface, we benchmark our multi-robot scenarios using various Proximal Policy Optimization (PPO)-based MARL algorithms. VMAS’s scenarios prove challenging in orthogonal ways for state-of-the-art MARL algorithms.

Publication
In The 16th International Symposium on Distributed Autonomous Robotic Systems (DARS)

Scenarios

Multi-robot scenarios introduced in VMAS. Robots (blue shapes) interact among each other and with landmarks (green, red, and black shapes) to solve a task.
Multi-robot scenarios introduced in VMAS. Robots (blue shapes) interact among each other and with landmarks (green, red, and black shapes) to solve a task.

Video

Matteo Bettini
Matteo Bettini
PhD Candidate

Matteo’s research is focused on studying heterogeneity and resilience in multi-agent and multi-robot systems.

Ryan Kortvelesy
Ryan Kortvelesy
PhD Candidate

Ryan’s work focuses on multi-agent reinforcement learning. He is interested in the credit assignment problem, new graph neural network architectures and explainability (applying symbolic regression to multi-agent systems).

Jan Blumenkamp
Jan Blumenkamp
PhD Candidate

Jan’s research is about transferring Multi-Agent control policies trained in simulation to the real world (sim-to-real transfer), using Multi-Agent Reinforcement Learning and Graph Neural Networks. He is also interested in interpretability, resilience and robustness of such control policies, particularly in the context of real-world systems.

Amanda Prorok
Amanda Prorok
Professor

Amanda’s research focuses on multi-agent and multi-robot systems. Our mission is to find new ways of coordinating artificially intelligent agents (e.g., robots, vehicles, machines) to achieve common goals in shared physical and virtual spaces.