Collaborative Robot Coordination Using Multi-Agent Reinforcement Learning

Abstract

Multi-Agent Reinforcement Learning (MARL) has emerged as an important approach for coordinating collaborative robots in industrial automation, warehouse logistics, healthcare, autonomous vehicles, and distributed robotic systems. Traditional centralized robot coordination methods faced limitations such as poor scalability, low adaptability, synchronization issues, and weak fault tolerance in dynamic environments. MARL overcomes these challenges through decentralized learning, where multiple robotic agents interact with the environment, learn from rewards, and improve coordination strategies autonomously. Before 2019, MARL gained significant attention in applications like cooperative navigation, formation control, multi-robot exploration, task allocation, path planning, collision avoidance, and resource sharing. This survey reviews key MARL techniques including Q-learning, Deep Q-Networks (DQN), policy-gradient methods, actor-critic models, and cooperative game-theoretic approaches for robotic coordination. The study explains a structured MARL coordination framework involving environment modeling, state representation, reward optimization, agent communication, and distributed decision-making. Experimental results show that MARL-based robotic systems improve task efficiency, coordination accuracy, energy optimization, adaptability, and collision reduction compared to centralized or heuristic methods. However, challenges such as communication delays, scalability, reward sparsity, non-stationary environments, and convergence instability still remain. The paper concludes that MARL is a promising solution for future intelligent collaborative robotics and highlights future research directions including federated reinforcement learning, explainable AI, edge-based robotic intelligence, and adaptive swarm robotics for Industry 4.0 applications.