<p>Efficient program execution on massively parallel clusters is critical for fields like scientific computing and artificial intelligence. However, traditional task scheduling algorithms do not fully leverage platform characteristics, resulting in inefficiency and long task execution times. We propose KANETAS, a reinforcement learning-based DAG (Directed Acyclic Graph) elastic task scheduling algorithm, designed to adapt to DAG tasks of various scales and structures. Kolmogorov-Arnold Network (KAN) was applied to the DAG scheduling problem. It enhances the efficiency of heterogeneous hardware by using Graph Convolutional Networks (GCN) and Actor-Critic Algorithm (A2C), recognizing hardware features and assigning tasks to appropriate computing units. We have conducted extensive experiments to evaluate the proposed solution with four strong baseline algorithms, including the state-of-the-art heuristics method and a variety of deep reinforcement learning based algorithms. The experimental results suggest that KANETAS can reduce the average makespan of the best baseline algorithm by 13.1% at most. Furthermore, compared to the MLP version, the KAN version showed superior performance. The proposed model demonstrates a clear advantage in load balancing.</p>