Autonomous Multi-Agent Systems Using Reinforcement Learning for Cooperative Task Allocation and Optimization

Abstract

In recent years, autonomous multi-agent systems (MAS) have become a viable paradigm for intelligent decision making and distributed problem solving in dynamic systems like smart warehouses, autonomous transportation, robotics, and industrial automation. Despite the importance and growing complexity of cooperative task allocation of multiple agents in today's environment, its efficiency is still a great challenge because of scalability problem, communication overhead, resource limitation, and continuously changing operational conditions. To tackle such problems, the current research investigates a reinforcement learning-based cooperative optimization approach to autonomous multi-agent coordination and adaptive task allocation applications. In the proposed system, the agents are based on Deep Q-Network (DQN) that can learn optimal task assignment policies by interacting continuously with the environment and by using reward-based feedback mechanisms. The multi-objective reward strategy aims to maximize the efficiency of task completion, energy consumption, and system stability. A smart warehouse automation case study with multiple autonomous robots in a dynamic task request and obstacle-rich environment is used to evaluate the framework.The framework is tested with a smart warehouse automation case study with multiple autonomous robots, dynamic task requests and obstacle-rich environment. Experimental results show that the task completion rate, latency, cooperative behavior, and energy consumption are better than the traditional scheduling methods. The proposed framework provides scalable, adaptive and real-time cooperative intelligence for next generation autonomous multi-agent applications.