Learning Portable Representations for High-Level Planning
Authors: Steven James, Benjamin Rosman, George Konidaris
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate transfer in two different domains, where an agent learns a portable, task-independent symbolic vocabulary, as well as operators expressed in that vocabulary, and then learns to instantiate those operators on a per-task basis. This reduces the number of samples required to learn a representation of a new task. |
| Researcher Affiliation | Academia | 1School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa 2Department of Computer Science, Brown University, Providence RI 02912, USA. |
| Pseudocode | No | The paper describes processes using flowcharts and text but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about open-sourcing its code. |
| Open Datasets | No | The paper introduces two custom domains, 'Rod-and-Block' and 'Treasure Game', stating 'We construct a domain we term Rod-and-Block' and 'We construct a set of ten tasks... of the Treasure Game'. No public access information (link, citation, repository) is provided for these datasets. |
| Dataset Splits | No | The paper describes how samples are collected and used for model building and evaluation but does not specify explicit train/validation/test splits (e.g., percentages or exact counts for data partitioning). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory, cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper mentions using DBSCAN, Support Vector Machine with Platt scaling, and Kernel Density Estimation, but does not provide specific version numbers for any of these software components or libraries. |
| Experiment Setup | No | The paper describes the general experimental procedure (e.g., collecting k transition samples, using a threshold of 0.75 for accuracy) but does not provide specific hyperparameter values or detailed system-level training settings for the models (e.g., learning rates, batch sizes, optimizer configurations). |