MQE: Unleashing the Power of Interaction with Multi-agent Quadruped Environment

TL;DR

The paper introduces MQE, a comprehensive multi-agent quadruped environment designed to facilitate research in multi-agent reinforcement learning with complex interactions, hierarchical policies, and realistic scenarios, bridging the gap between simulation and real-world deployment.

Contribution

It presents a novel platform, MQE, for developing and evaluating multi-agent reinforcement learning algorithms in dynamic, realistic quadruped robot scenarios, including benchmarks and diverse tasks.

Findings

Hierarchical reinforcement learning simplifies task learning.

Advanced algorithms are needed for complex multi-agent interactions.

MQE effectively models real-world multi-robot scenarios.

Abstract

The advent of deep reinforcement learning (DRL) has significantly advanced the field of robotics, particularly in the control and coordination of quadruped robots. However, the complexity of real-world tasks often necessitates the deployment of multi-robot systems capable of sophisticated interaction and collaboration. To address this need, we introduce the Multi-agent Quadruped Environment (MQE), a novel platform designed to facilitate the development and evaluation of multi-agent reinforcement learning (MARL) algorithms in realistic and dynamic scenarios. MQE emphasizes complex interactions between robots and objects, hierarchical policy structures, and challenging evaluation scenarios that reflect real-world applications. We present a series of collaborative and competitive tasks within MQE, ranging from simple coordination to complex adversarial interactions, and benchmark state-of-the-art MARL algorithms. Our findings indicate that hierarchical reinforcement learning can simplify task learning, but also highlight the need for advanced algorithms capable of handling the intricate dynamics of multi-agent interactions. MQE serves as a stepping stone towards bridging the gap between simulation and practical deployment, offering a rich environment for future research in multi-agent systems and robot learning. For open-sourced code and more details of MQE, please refer to https://ziyanx02.github.io/multiagent-quadruped-environment/ .