Truly Batch Apprenticeship Learning with Deep Successor Features
Authors: Donghun Lee, Srivatsan Srinivasan, Finale Doshi-Velez
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our model s superior performance in batch settings with both classical control tasks and a real-world clinical task of sepsis management in the ICU. |
| Researcher Affiliation | Academia | Donghun Lee , Srivatsan Srinivasan and Finale Doshi-Velez SEAS, Harvard University {srivatsansrinivasan, donghunlee}@g.harvard.edu, finale@seas.harvard.edu, |
| Pseudocode | Yes | Algorithm 1 Batch Max-Margin IRL |
| Open Source Code | Yes | Link to our code repository can be found in Section 4 of the Appendix. |
| Open Datasets | Yes | The input data was obtained from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-III v1.4) database [Johnson et al., 2016] and included a cohort of 17,898 patients fulfilling Sepsis-3 criteria. |
| Dataset Splits | Yes | We followed a 70 30 train-validation split in our batch data. AND We then performed a 60-20-20 train-val-test split on our dataset. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like DDQN and OpenAI Gym environments but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | Parametric details of all the models are in Appendix Table 2. |