# Efficient Task Allocation in Multi-Agent Systems Using Reinforcement Learning and Genetic Algorithm
## 4 A Learning Algorithm
### Genetic Algorithm-Enhanced Proximal Policy Optimization
Future research could explore strategies such as adaptive model complexity or decentralized learning to further minimize computational overhead while maintaining performance. Overall, the results demonstrate that GAPPO effectively manages energy consumption across different scales and battery configurations while maintaining performance as task complexity and agent numbers increase. Its ability to balance task allocation and energy efficiency makes it well suited for real-world applications with constrained computational and energy resources. This robustness highlights its potential for deployment in complex multi-agent environments requiring high efficiency across multiple dimensions. In the conducted experiments, GAPPO outperformed the baseline algorithms, including PPO, DDPG, DQN, and A2C, across various metrics such as task completion time, resource efficiency, and scalability. GAPPO's superior performance is attributed to its ability to combine the strengths of both reinforcement learning and evolutionary algorithms. The genetic algorithm component enables a more diverse exploration of the policy

\[A(s_{t},a_{t})=Q(s_{t},a_{t})-V(s_{t}),\]0 Comparison of five algorithms on power remaining percentage after task completion. space, leading to more robust solutions, particularly in dynamic environments where task configurations and agent capabilities vary over time. Compared to PPO and A2C, which rely solely on policy gradient algorithms, GAPPO benefits from the genetic algorithm's ability to select and evolve the most promising policies across generations. This evolution not only improves the exploration of the solution space but also ensures better convergence, particularly in complex, multi-agent environments where standard reinforcement learning algorithms often struggle to balance exploration and exploitation. DDPG and DQN, being value-based algorithms, face limitations in environments with continuous action spaces or complex task dependencies. Those algorithms are more prone to getting stuck in local optima due to their reliance on fixed Q-values or deterministic policies. In contrast, GAPPO overcomes those limitations by combining both policy gradient and evolutionary search. This flexibility allows it to dynamically adjust its policies to adapt to changes in the environment and task complexity, leading to improved performance in a wider range of scenarios. In summary, GAPPO's integration of a genetic algorithm with reinforcement learning enables it to effectively balance exploration and exploitation, outperforming traditional algorithms like PPO, DDPG, DQN, and A2C in terms of scalability, adaptability, and robustness. This makes GAPPO particularly well suited for dynamic multi-agent systems, where environment complexity and agent interactions are key factors for success. ## 6 Conclusions and Future Work

In conclusion, the GAPPO algorithm stands out as a robust solution to the challenges inherent in multi-agent task allocation within complex environments. Traditional algorithms often encounter significant limitations in scalability and adaptability, struggling to manage the intricacies associated with numerous agents and tasks efficiently. In contrast, the GAPPO algorithm effectively addresses these shortcomings by prioritizing energy efficiency and optimal resource distribution, leading to superior performance outcomes. Our comprehensive experimental results consistently demonstrate that GAPPO algorithm significantly reduces task completion time while maintaining high energy levels across agent fleets. This effectiveness highlights the algorithm's capability to enhance operational efficiency in diverse scenarios, solidifying its role in advancing multi-agent systems.