Action Selection Methods for Multi-Agent Navigation in Crowded Environments
Authors: Julio Godoy
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results obtained in simulation under different conditions show that the agents reach their destinations faster and use motions that minimize their overall energy consumption. and Figure 2 shows the results (in Interaction Overhead) of comparing two of my proposed approaches, ALAN and C-Nav, with ORCA in three example environments. |
| Researcher Affiliation | Academia | Julio Godoy Department of Computer Science and Engineering University of Minnesota |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks are present in the paper. |
| Open Source Code | No | No explicit statement or link for open-source code release for the described methodology is provided. |
| Open Datasets | No | No specific publicly available dataset is mentioned with a link, DOI, or formal citation (including author names and year in brackets/parentheses). The paper refers to 'simulation under different conditions' and 'three example environments' (Circle, Bidirectional, Crowd), but these are descriptions of scenarios rather than external datasets with access information. |
| Dataset Splits | No | No specific dataset split information (percentages, counts, or predefined splits) is provided for training, validation, or testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names with versions like Python 3.8, PyTorch 1.9) are provided. |
| Experiment Setup | No | The paper describes the environments used for evaluation (Figure 2a) and mentions a new metric 'Interaction Overhead' for comparison, but it does not provide specific hyperparameters (e.g., learning rate, batch size, epochs) or detailed system-level training settings. |