TRATSS: Transformer-Based Task Scheduling System for Autonomous Vehicles
This addresses scheduling challenges for autonomous vehicles, but it appears incremental as it builds on existing transformer and reinforcement learning methods.
The paper tackles the problem of efficient scheduling for autonomous vehicles by introducing TRATSS, a transformer-based system that optimizes task scheduling in graph-based environments, demonstrating effectiveness on benchmark datasets with enhanced resource utilization and task completion efficiency.
Efficient scheduling remains a critical challenge in various domains, requiring solutions to complex NP-hard optimization problems to achieve optimal resource allocation and maximize productivity. In this paper, we introduce a framework called Transformer-Based Task Scheduling System (TRATSS), designed to address the intricacies of single agent scheduling in graph-based environments. By integrating the latest advancements in reinforcement learning and transformer architecture, TRATSS provides a novel system that outputs optimized task scheduling decisions while dynamically adapting to evolving task requirements and resource availability. Leveraging the self-attention mechanism in transformers, TRATSS effectively captures complex task dependencies, thereby providing solutions with enhanced resource utilization and task completion efficiency. Experimental evaluations on benchmark datasets demonstrate TRATSS's effectiveness in providing high-quality solutions to scheduling problems that involve multiple action profiles.