Pareto Inverse Reinforcement Learning for Diverse Expert Policy Generation
Authors: Woo Kyung Kim, Minjong Yoo, Honguk Woo
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Evaluation, Table 1: Performance of Pareto set generation, Figure 4: Pareto policy set Π visualization and learning curve |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering, Sungkyunkwan University {kwk2696, mjyoo2, hwoo}@skku.edu |
| Pseudocode | Yes | Algorithm 1 Recursive reward distance regularized IRL |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper mentions collecting 'distinct datasets' and 'expert datasets' from environments like MO-Car and CARLA, but it does not provide specific links, DOIs, or clear citations for public access to these datasets. |
| Dataset Splits | No | The paper does not explicitly state train/validation/test dataset splits with percentages, sample counts, or references to predefined standard splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions the CARLA simulator but does not specify version numbers for any software dependencies, libraries, or programming frameworks used in the experiments. |
| Experiment Setup | No | The paper mentions that 'β is a hyperparameter' but does not provide specific values for it or other crucial experimental setup details such as learning rates, batch sizes, or optimizer settings. |