# Efficient Task Allocation in Multi-Agent Systems Using Reinforcement Learning and Genetic Algorithm
###### Abstract  
The multi-agent task allocation problem has attracted substantial research interest due to the increasing demand for effective solutions in large-scale and dynamic environments. Despite advancements, traditional algorithms often fall short in optimizing efficiency and adaptability within complex scenarios. To address this shortcoming, we propose a genetic algorithm-enhanced PPO (GAPPO) algorithm, specifically developed to enhance decision-making in challenging task allocation contexts. GAPPO employs a deep reinforcement learning framework, enabling each agent to independently evaluate its surroundings, manage energy resources, and adaptively adjust task allocations in response to evolving conditions. Through iterative refinement, GAPPO achieves balanced task distribution and minimizes energy consumption across diverse configurations. Comprehensive simulations demonstrate that GAPPO consistently outperforms traditional algorithms, resulting in reduced task completion time and heightened energy efficiency. Our findings underscore GAPPO's potential as a robust solution for real-time multi-agent task allocation. m +
Footnote †: 2020: _Applied sciences_  
enabling agents to learn allocation strategies through interaction with the environment. However, conventional RL algorithms often suffer from slow convergence and instability, particularly in large-scale and dynamic settings [19,20]. The multi-agent task allocation problem considered in this work is computationally challenging due to its combinatorial nature. The problem requires determining an optimal task allocation policy while considering constraints such as agent capabilities, dynamic task demands, and communication limitations. Similar combinatorial optimization problems, such as the multi-agent traveling salesman problem (mTSP) and job-shop scheduling, have been shown to be NP-hard [21]. Given the exponential growth of the solution space as the number of agents and tasks increases, solving this problem optimally in real time becomes computationally infeasible with traditional optimization techniques. To overcome those limitations, we propose a novel algorithm that integrates genetic algorithms (GAs) with proximal policy optimization (PPO) to enhance task allocation efficiency in multi-agent systems. The GA facilitates exploration through selection, crossover, and mutation, improving policy diversity and search effectiveness. Meanwhile, PPO optimizes decision-making by leveraging real-time feedback, enabling agents to dynamically adjust allocations. This hybrid algorithm enhances adaptability and learning efficiency in dynamic task allocation scenarios. By combining the GA's exploratory strengths with PPO's optimization capabilities, our algorithm addresses the challenges posed by traditional RL algorithms and provides a more scalable solution to the task allocation problem. The main contributions of this paper are as follows. 1. We model the multi-agent task allocation problem as a Markov game, where each agent functions as an independent agent, interacting continuously with its environment to iteratively improve its task allocation. Under this framework, each agent determines its actions based on local observations, facilitating coordinated and efficient task distribution across the swarm. 2. We propose a GA-PPO reinforcement learning algorithm, which combines genetic algorithms with proximal policy optimization, incorporating an attention mechanism and adaptive learning rate. This algorithm empowers each agent to autonomously adapt to changing task demands, improving learning efficiency and facilitating effective coordination with multi-agent environments. By leveraging these features, the system optimizes resource utilization while maintaining robust performance in dynamic scenarios. 3. Numerical experiments demonstrate that our proposed algorithm performs well in terms of convergence speed and scalability compared with the existing representative algorithms. This paper is organized as follows. Section 2 reviews related work.