Objective-aware Traffic Simulation via Inverse Reinforcement Learning
Authors: Guanjie Zheng, Hanyang Liu, Kai Xu, Zhenhui Li
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic and real-world datasets show the superior performance of our approach compared to state-of-the-art methods and its robustness to variant dynamics of traffic. |
| Researcher Affiliation | Collaboration | 1Shanghai Jiao Tong University 2Washington University in St. Louis 3Shanghai Tianrang Intelligent Technology Co., Ltd 4The Pennsylvania State University |
| Pseudocode | Yes | Algorithm 1: Training procedure of PS-AIRL |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | We evaluate our proposed model on 3 different real-world traffic trajectory datasets with distinct road network structures collected from Hangzhou of China, and Los Angeles of US, including 3 typical 4-way intersections, a 4 4 network, and a 1 4 arterial network. See Appendix for details. The appendix will be released on the authors website and arxiv. The paper mentions specific datasets used but does not provide a direct link, DOI, or explicit citation for public access within the provided text. The appendix is mentioned as being released externally, which is not concrete access within the paper itself. |
| Dataset Splits | No | The paper mentions evaluating models on datasets but does not specify training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions tools like SUMO, AIMSUN, MITSIM, and TRPO but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper states, 'See Appendix for details' regarding data and experiment settings. While it mentions the use of TRPO as a policy optimizer and changes in simulation dynamics for robustness testing, it does not provide specific hyperparameters (e.g., learning rate, batch size) or detailed training configurations for its proposed PS-AIRL model within the main text. |