让多个真实机器人在对抗与合作中高效学习并成功落地。
Cooperative-Competitive Team Play of Real-World Craft Robots
- 构建仿真-分布式训练-物理机器人一体化系统，支持多智能体强化学习。
- 引入OODSI方法，使真实环境表现提升20%，显著缩小仿真到现实的差距。
- 适用于需要多机器人协作或竞争的工业场景，如仓储物流、智能巡检。
近年来，多智能体深度强化学习在开发智能游戏对战代理方面取得了显著进展。然而，如何高效训练群体机器人并将其学习策略迁移到真实世界应用仍是未解难题。本文首先构建了一个完整的机器人系统，包含仿真环境、分布式学习框架和实体机器人组件。随后，提出并评估了针对该平台设计的强化学习技术，以实现合作与竞争策略的高效训练。为应对多智能体仿真到现实迁移的挑战，提出分布外状态初始化（OODSI）方法，有效缓解仿真与现实之间的差异。实验表明，该方法使仿真到现实的表现提升20%。通过在真实世界环境中进行多机器人小车竞技游戏与协作任务的实验，验证了所提方法的有效性。
原文摘要 · Abstract (English)
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.
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