# Preference-Conditioned Multi-Objective Reinforcement Learning for Runtime-Tunable Transit Signal Priority

arXiv: https://arxiv.org/abs/2607.18286 · HTML: https://mlsanity.com/paper/2607.18286/

## 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 10 of its 22 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation](https://mlsanity.com/paper/1908.08342/) (2019)
- [Dynamic Weights in Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1809.07803/) (2018)
- [Pareto Conditioned Networks](https://mlsanity.com/paper/2204.05036/) (2022)
- [GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning](https://mlsanity.com/paper/2009.08052/) (2020)
- [LibSignal: An Open Library for Traffic Signal Control](https://mlsanity.com/paper/2211.10649/) (2022)
- [IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning](https://mlsanity.com/paper/2410.15221/) (2024)
- [Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems](https://mlsanity.com/paper/2104.10455/) (2021)
- [Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning](https://mlsanity.com/paper/2307.11357/) (2023)

## Abstract (arXiv)

Transit signal priority (TSP) requires balancing competing objectives: reducing bus delay while limiting adverse impacts on non-bus traffic and avoiding extreme waits for a subset of vehicles. Existing reinforcement-learning (RL) approaches to TSP typically encode transit-aware features (e.g., occupancy and schedule deviation) but optimize a fixed reward or fixed scalarization, which limits operational flexibility when agency priorities change across time-of-day or disruption conditions. We present a preference-conditioned TSP controller, π(a mid s,w), that selects the next signal phase under minimum/maximum green and transition-feasibility constraints and can be tuned at runtime via a preference parameter w to trade off bus-priority emphasis against overall traffic delay without retraining. We implement this on top of IntersectionZoo by introducing a constrained signal-control/TSP wrapper, and we extend scenario generation with bus-prevalence augmentation and timetable-based bus insertion to address sparse transit-priority events during training. Experiments against fixed-time control, a rule-based TSP overlay, and fixed-weight PPO specialists show that a single learned conditioned policy spans a smooth empirical trade-off frontier across runtime preferences, outperforms fixed-time and rule-based baselines, and maintains constraint feasibility, while tail-delay diagnostics reveal that non-bus externalities remain limited for moderate preference settings but can increase substantially under high bus-priority weights. The source code of this work is available at https://github.com/urbanAIthi/morl-tsp.