Spatial-Aware Deep Reinforcement Learning for the Traveling Officer Problem
This work addresses a domain-specific problem for urban management and parking enforcement, offering an incremental improvement over existing solutions.
The paper tackles the traveling officer problem, a stochastic optimization task where a parking officer must dynamically fine parking offenses that appear and disappear randomly, by proposing SATOP, a spatial-aware deep reinforcement learning approach that fine up to 22% more parking offenses compared to state-of-the-art methods.
The traveling officer problem (TOP) is a challenging stochastic optimization task. In this problem, a parking officer is guided through a city equipped with parking sensors to fine as many parking offenders as possible. A major challenge in TOP is the dynamic nature of parking offenses, which randomly appear and disappear after some time, regardless of whether they have been fined. Thus, solutions need to dynamically adjust to currently fineable parking offenses while also planning ahead to increase the likelihood that the officer arrives during the offense taking place. Though various solutions exist, these methods often struggle to take the implications of actions on the ability to fine future parking violations into account. This paper proposes SATOP, a novel spatial-aware deep reinforcement learning approach for TOP. Our novel state encoder creates a representation of each action, leveraging the spatial relationships between parking spots, the agent, and the action. Furthermore, we propose a novel message-passing module for learning future inter-action correlations in the given environment. Thus, the agent can estimate the potential to fine further parking violations after executing an action. We evaluate our method using an environment based on real-world data from Melbourne. Our results show that SATOP consistently outperforms state-of-the-art TOP agents and is able to fine up to 22% more parking offenses.