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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Covering Number for Efficient Heuristic-based POMDP Planning
Authors: Zongzhang Zhang, David Hsu, Wee Sun Lee
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare PGVI with some existing point-based algorithms in their performance on 65 out of the 68 small benchmark problems from Cassandra s POMDP website1 and 4 larger robotic problems (Ross et al., 2008; Hsu et al., 2008; Kurniawati et al., 2008; 2011). Empirically, PGVI is competitive with the state-of-the-art point-based POMDP algorithms on 65 small benchmark problems and outperforms them on 4 larger problems. |
| Researcher Affiliation | Academia | Zongzhang Zhang EMAIL David Hsu EMAIL Wee Sun Lee EMAIL Department of Computer Science, National University of Singapore, Singapore 117417, Singapore |
| Pseudocode | Yes | Algorithm 1 π = PGVI(ϵ, δ). Algorithm 2 EXPLORE(b, db, ϵ, δ). |
| Open Source Code | No | We used the APPL-0.95 software package to implement the PGVI algorithm, but did not use the MOMDP representation (Ong et al., 2010). http://bigbird.comp.nus.edu.sg/pmwiki/farm/appl/ (This refers to a third-party software package used for implementation, not an explicit release of the authors' own code for PGVI.) |
| Open Datasets | Yes | We compare PGVI with some existing point-based algorithms in their performance on 65 out of the 68 small benchmark problems from Cassandra s POMDP website1 and 4 larger robotic problems (Ross et al., 2008; Hsu et al., 2008; Kurniawati et al., 2008; 2011). 1http://www.pomdp.org |
| Dataset Splits | No | The paper does not provide specific details about train/validation/test dataset splits (e.g., percentages, sample counts, or cross-validation setup) for the problems used in the experiments. |
| Hardware Specification | No | Our experimental platform is a CPU at 2.40GHz, with 3GB memory. (This provides some specifications but lacks specific CPU model details.) |
| Software Dependencies | Yes | We used the APPL-0.95 software package2 to implement the PGVI algorithm |
| Experiment Setup | Yes | We set δ = (tmax t)δ0/tmax, where δ0 = 0.5, tmax represents the upper bound of running time, and t represents the elapsed time in running PGVI, to make PGVI do the best in the available time. Given that the value of δ changes with time, we use the simpler value of excess(b, db, ϵ) = V U(b) V L(b) ϵ/γdb to terminate trials. ... In PGVI and SARSOP, ϵ is set to 0.5 [V U(b0) V L(b0)] in the beginning of each trial. |