Curiosity Driven Reinforcement Learning for Job Shop Scheduling
Year: 2025
Type: conference-paper
Abstract: The Job Shop Problem (JSP) is a well-known NP-hard problem with numerous applications in manufacturing and other fields.Efficient scheduling is critical for producing customized products in the manufacturing industry in time.Typically, the quality metrics of a schedule, such as the makespan, can only be assessed after all tasks have been assigned, leading to sparse reward signals when framing JSP as a reinforcement learning (RL) problem.Sparse rewards pose significant challenges for many RL algorithms, often resulting in slow learning behavior.Curiosity algorithms, which introduce intrinsic reward signals, have been shown to accelerate learning in environments with sparse rewards.In this study, we explored the effectiveness of the Intrinsic Curiosity Module (ICM) and Episodic Curiosity (EC) by benchmarking them against state-of-the-art methods.Our experiments demonstrate that the use of curiosity significantly increases the amount of states encountered by the RL agent.When the intrinsic and extrinsic reward signals are of comparable magnitude, the agent is with ICM module are able to escape local optima and discover better solutions.
Institution: RWTH Aachen University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408062225