List of Publications

List of publications that use SUMO-RL (please open a pull request to add missing entries):

• Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control (Alegre et al., 2021)

• Information-Theoretic State Space Model for Multi-View Reinforcement Learning (Hwang et al., 2023)

• A citywide TD-learning based intelligent traffic signal control for autonomous vehicles: Performance evaluation using SUMO (Reza et al., 2023)

• Handling uncertainty in self-adaptive systems: an ontology-based reinforcement learning model (Ghanadbashi et al., 2023)

• Multiagent Reinforcement Learning for Traffic Signal Control: a k-Nearest Neighbors Based Approach (Almeida et al., 2022)

• From Local to Global: A Curriculum Learning Approach for Reinforcement Learning-based Traffic Signal Control (Zheng et al., 2022)

• Poster: Reliable On-Ramp Merging via Multimodal Reinforcement Learning (Bagwe et al., 2022)

• Using ontology to guide reinforcement learning agents in unseen situations (Ghanadbashi & Golpayegani, 2022)

• Information upwards, recommendation downwards: reinforcement learning with hierarchy for traffic signal control (Antes et al., 2022)

• A Comparative Study of Algorithms for Intelligent Traffic Signal Control (Chaudhuri et al., 2022)

• An Ontology-Based Intelligent Traffic Signal Control Model (Ghanadbashi & Golpayegani, 2021)

• Reinforcement Learning Benchmarks for Traffic Signal Control (Ault & Sharon, 2021)

• EcoLight: Reward Shaping in Deep Reinforcement Learning for Ergonomic Traffic Signal Control (Agand et al., 2021)