Collaborative Decision Making Using Action Suggestions
Authors: Dylan Asmar, Mykel J Kochenderfer
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose two methods that use suggested actions and demonstrate the approach through simulated experiments. The proposed methodology results in increased performance while also being robust to suboptimal suggestions. |
| Researcher Affiliation | Academia | Dylan M. Asmar Stanford Intelligent Systems Laboratory Stanford University Stanford, CA 94305 asmar@stanford.edu Mykel J. Kochenderfer Stanford Intelligent Systems Laboratory Stanford University Stanford, CA 94305 mykel@stanford.edu |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | Yes | Code is available at https://github.com/sisl/action_suggestions. |
| Open Datasets | Yes | The proposed methodology to incorporate action suggestions as observations was evaluated on two classic POMDP problems, Tag [30] and Rock Sample [38]. |
| Dataset Splits | No | No explicit train/validation/test dataset splits are mentioned, as the experiments are conducted in simulated POMDP environments rather than on fixed datasets that are typically split for supervised learning. |
| Hardware Specification | No | Our approach does not require a lot of computation. Computation time and amount was not a concern and not provided. |
| Software Dependencies | No | The simulation environment was built using the POMDPs.jl framework [39]. |
| Experiment Setup | Yes | Experiment details are documented in section 4. The hyperparameter is kept constant for each simulation and the value used is shown with the presented results. |