Learning Models of Sequential Decision-Making with Partial Specification of Agent Behavior
Authors: Vaibhav V. Unhelkar, Julie A. Shah2522-2530
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach in experiments and demonstrate improvements in metrics of model alignment. |
| Researcher Affiliation | Academia | Vaibhav V. Unhelkar, Julie A. Shah Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA 02139 {unhelkar, julie a shah}@csail.mit.edu |
| Pseudocode | No | The paper describes algorithms and equations but does not present any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for the datasets used. It describes simulated scenarios, but not how to access the data. |
| Dataset Splits | No | The paper mentions 'training and testing datasets' and 'test set' but does not provide specific percentages, sample counts, or a detailed methodology for splitting the data to reproduce the partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'pyhsmm, a Python library for approximate unsupervised inference' and 'SLSQP, a sequential least squares programming optimization algorithm as implemented in scipy', but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Identical priors, hyper-parameters, initialization and termination conditions were used for all the algorithms. |