Spatial-Aware Deep Reinforcement Learning for the Traveling Officer Problem
On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 22 references that are in the map. First posted on arXiv 11 Jan 2024.
Builds on
13 of 22 references.
References · 13
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- 2026Adapting Reinforcement Learning for Path Planning in Constrained Parking Scenarios
- 2025Dynamic Configuration of On-Street Parking Spaces using Multi Agent Reinforcement Learning
- 2026NeuralParker: A Reinforcement Learning Planner for Irregular Parking Environments
- 2025A Diffusion-Refined Planner with Reinforcement Learning Priors for Confined-Space Parking
- 2024Speeding Up Path Planning via Reinforcement Learning in MCTS for Automated Parking
- 2025Automated Parking Trajectory Generation Using Deep Reinforcement Learning
- 2026REAP: Reinforcement-Learning End-to-End Autonomous Parking with Gaussian Splatting Simulator for Real2Sim2Real Transfer
- 2025SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning