Rebalancing Expanding EV Sharing Systems with Deep Reinforcement Learning
Authors: Man Luo, Wenzhe Zhang, Tianyou Song, Kun Li, Hongming Zhu, Bowen Du, Hongkai Wen
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed approach using a simulator calibrated with 1-year operation data from a real EV sharing system. Results show that our approach significantly outperforms the state-of-the-art, offering up to 14% gain in order satisfied rate and 12% increase in net revenue. We build a simulator which is calibrated with 12 months operation data collected from a real-world EV sharing system . The proposed approach has been evaluated extensively, and results show that it significantly outperforms the state-of-the-art, offering up to 12% improvement in net revenue and 14% in demand satisfied rate. |
| Researcher Affiliation | Collaboration | Man Luo1,2 , Wenzhe Zhang3 , Tianyou Song3 , Kun Li3 , Hongming Zhu3 , Bowen Du1 and Hongkai Wen1 1Department of Computer Science, University of Warwick, UK 2The Alan Turing Institute, UK 3School of Software Engineering, Tongji University, China |
| Pseudocode | No | The paper describes the proposed approach (e.g., in Section 3.2 "Policy Optimization with Action Cascading") but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/ev-sharing/simulator. |
| Open Datasets | No | We build a simulator which is calibrated with 12 months operation data collected from a real-world EV sharing system . The paper states the data is from a "real-world EV sharing system" in Shanghai, but provides no link or citation for public access to this specific dataset. |
| Dataset Splits | No | The paper discusses using a simulator calibrated with real-world data and training a neural network. However, it does not provide specific details on how the dataset (simulated or real) is split into training, validation, and test sets for the MARL algorithm itself. |
| Hardware Specification | Yes | All the competing approaches are implemented with Tensor Flow 1.14.0, and trained with a single NVIDIA 2080Ti GPU. |
| Software Dependencies | Yes | All the competing approaches are implemented with Tensor Flow 1.14.0, and trained with a single NVIDIA 2080Ti GPU. |
| Experiment Setup | Yes | The weights α1, α2 and α3 scale different reward/penalty terms to approximately the same range, which are determined empirically via grid search. ϵ is a hyperparameter (usually set to 0.2 0.3). |