Cooperative-Competitive Team Play of Real-World Craft Robots

Rui Zhao, Xihui Li, Yizheng Zhang, Yuzhen Liu, Zhong Zhang, Yufeng Zhang, Cheng Zhou, Zhengyou Zhang, Lei Han · Feb 24, 2026 · Citations: 0

General Multi Agent Simulation Env

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Abstract

Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years. However, the efficient training of collective robots using multi-agent RL and the transfer of learned policies to real-world applications remain open research questions. In this work, we first develop a comprehensive robotic system, including simulation, distributed learning framework, and physical robot components. We then propose and evaluate reinforcement learning techniques designed for efficient training of cooperative and competitive policies on this platform. To address the challenges of multi-agent sim-to-real transfer, we introduce Out of Distribution State Initialization (OODSI) to mitigate the impact of the sim-to-real gap. In the experiments, OODSI improves the Sim2Real performance by 20%. We demonstrate the effectiveness of our approach through experiments with a multi-robot car competitive game and a cooperative task in real-world settings.

Human Data Lens

Uses human feedback: No
Feedback types: None
Rater population: Unknown
Unit of annotation: Unknown
Expertise required: General

Evaluation Lens

Evaluation modes: Simulation Env
Agentic eval: Multi Agent
Quality controls: Not reported
Confidence: 0.40
Flags: ambiguous

Research Summary

Contribution Summary

Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years.
However, the efficient training of collective robots using multi-agent RL and the transfer of learned policies to real-world applications remain open research questions.
In this work, we first develop a comprehensive robotic system, including simulation, distributed learning framework, and physical robot components.

Why It Matters For Eval

Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years.
However, the efficient training of collective robots using multi-agent RL and the transfer of learned policies to real-world applications remain open research questions.