Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Multi-Objective POMDPs with Lexicographic Reward Preferences
Authors: Kyle Hollins Wray, Shlomo Zilberstein
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test the algorithms using real-world road data provided by Open Street Map (OSM) within 10 major cities. Finally, we present GPU-based optimizations for point-based solvers, demonstrating that their application enables us to quickly solve vastly larger LPOMDPs and other variations of POMDPs. |
| Researcher Affiliation | Academia | Kyle Hollins Wray and Shlomo Zilberstein College of Information and Computer Sciences University of Massachusetts, Amherst, MA 01003 EMAIL |
| Pseudocode | No | The paper describes algorithms using equations and narrative, but no explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | No | Additionally, we plan to make our domain-specific tools public, in addition to both our CPU and GPU source code, to facilitate community-wide development of realistic-scale domains and algorithms for planning under partial observability. |
| Open Datasets | Yes | We test the algorithms using real-world road data provided by Open Street Map (OSM) within 10 major cities. |
| Dataset Splits | No | The paper does not provide explicit details about dataset splits for training, validation, or testing. |
| Hardware Specification | Yes | Experiments were conducted with an Intel(R) Core(TM) i7-4702HQ CPU at 2.20GHz, 8GB of RAM, and an Nvidia(R) Ge Force GTX 870M graphics card using C++ and CUDA(C) 6.5. |
| Software Dependencies | Yes | Experiments were conducted with an Intel(R) Core(TM) i7-4702HQ CPU at 2.20GHz, 8GB of RAM, and an Nvidia(R) Ge Force GTX 870M graphics card using C++ and CUDA(C) 6.5. |
| Experiment Setup | Yes | Table 1: Computation time (seconds), and the initial belief s values (negated travel time; seconds) over 10 cities for LPBVI on the CPU (h 10) and GPU (h 500) and the improvement ratio: p50 CPUq{GPU adjusted for the horizon difference. These are point-based algorithms without expansion, so each horizon step is the same operation. |