Solving Partially Observable Stochastic Shortest-Path Games
Authors: Petr Tomášek, Karel Horák, Aditya Aradhye, Branislav Bošanský, Krishnendu Chatterjee
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate the algorithm on a pursuit-evasion game. |
| Researcher Affiliation | Academia | 1Artificial Intelligence Center, Dept. of Computer Science Faculty of Electrical Engineering, Czech Technical University in Prague 2Institute of Science and Technology Austria |
| Pseudocode | Yes | Algorithm 1: HSVI for discounted OS-POSGs; Algorithm 2: HSVI algorithm for POSSPGs |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | The paper describes using a 'pursuit-evasion game' environment for evaluation, which is a simulated setting rather than a named, publicly available dataset with concrete access information (link, DOI, etc.). |
| Dataset Splits | No | The paper describes the setup of a pursuit-evasion game but does not specify any training/validation/test dataset splits for reproducibility. |
| Hardware Specification | Yes | All computational results have been obtained on computers equipped with Intel Xeon Scalable Gold 6146 processors while limiting the runtime to 10 hours and RAM to 128 GB. |
| Software Dependencies | Yes | We used CPLEX 12.9 to solve linear programs. |
| Experiment Setup | Yes | All solution methods were required to find an ϵ-optimal solution where ϵ was set to 1. Since the reward for all transitions in the game is 1, such setting allows us to find an optimal solution 1 move. |