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..
Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games
Authors: Julien Perolat, Bruno Scherrer, Bilal Piot, Olivier Pietquin
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate experimentally the performance of AGPIQ on a simultaneous two-player game, namely Alesia.In this section, AGPI-Q is tested on the Alesia game described in Sec. 2.5 where we assume that both players start with a budget n = 20. As a baseline, we use the exact solution of the problem provided by VI. We have run the algorithm for K = 10 iterations and for m {1, 2, 3, 4, 5} evaluation steps. We have considered different sample set sizes, N = 2500, 5000, 10000. Each experiment is repeated 20 times. |
| Researcher Affiliation | Academia | (1)Univ. Lille, CRISt AL, Seque L team, France (2)Inria, Villers-l es-Nancy, F-54600, France (3)Institut Universitaire de France (IUF), France |
| Pseudocode | Yes | Algorithm 1 AGPI Q for Batch sample Input: ((xj, aj 1, aj 2), rj, x j)j=1,...,N some samples, q0 = 0 a Q-function, F an hypothesis space for k=1,2,...,K do Greedy step: for all j do aj = arg max a min b qk 1(x j, a, b) (solving a matrix game) end for Evaluation step: qk,0 = qk 1 for i=1,...,m do for all j do qj = r(xj, aj 1, aj 2) + γ minb qk,i 1(x j, aj, b) end for qk,i = arg minq F j=1 l(q(xj, aj 1, aj 2), qj) Where l is a loss function. qk = qk,m end for end for output q K |
| 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 in this paper. |
| Open Datasets | No | The paper describes the Alesia game and how samples were generated, but does not provide concrete access information (link, DOI, repository name, or formal citation with authors/year) for a publicly available or open dataset used in the experiments. |
| Dataset Splits | No | The paper describes generating N uniform samples and using them in a batch setting, but does not specify explicit training, validation, or test dataset splits for these samples. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using CART trees and linear programming techniques, but does not specify any software names with version numbers for replication. |
| Experiment Setup | Yes | We have run the algorithm for K = 10 iterations and for m {1, 2, 3, 4, 5} evaluation steps. We have considered different sample set sizes, N = 2500, 5000, 10000. Each experiment is repeated 20 times. |