Bidirectional Q-learning for recycling path planning of used appliances under strong and weak constraints
Year: 2024
Type: article
Abstract: With the continuous innovation in household appliance technology and the improvement of living standards , the production of discarded household appliances has rapidly increased, making their recycling increasingly significant. Traditional path planning algorithms encounter difficulties in balancing efficiency and constraints in addressing the multi-objective, multi-constraint challenge posed by discarded household appliance recycling routes. To tackle this issue, this study introduces a bi-directional Q -learning-based path planning algorithm. By developing a bi-directional Q -learning mechanism and enhancing the initialization method of Q -learning, the algorithm aims to achieve efficient and effective optimization of discarded household appliance recycling routes. It implements bidirectional updates of the state-action value function from both the starting point and the target point. Additionally, a hierarchical reinforcement learning strategy and guided rewards are introduced to minimize blind exploration and expedite convergence. By decomposing complex recycling tasks into multiple sub-tasks and seeking paths with superior performance at each sub-task level, the initial exploratory blindness is reduced. To validate the efficacy of the proposed algorithm, gridbased modeling of real-world environments is utilized. Comparative experiments reveal significant improvements in iteration counts and path lengths, thereby validating its practical applicability in path planning for recycling initiatives.
Authors: Yang Qi, Jinxin Cao, Baijing Wu
Institutions: Inner Mongolia University, Lanzhou Jiaotong University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,250
OpenAlex ID: W4404756542