COG-DICE: An Algorithm for Solving Continuous-Observation Dec-POMDPs

Authors: Madison Clark-Turner, Christopher Amato

IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Our approach is evaluated on a novel tagging problem and a modified version of the recycling robots benchmark. The COG-DICE implementation was tested on two different domains: a tagging simulator and a recycling robots problem.
Researcher Affiliation Academia Madison Clark-Turner Department of Computer Science University of New Hampshire Durham, NH 03824 mbc2004@cs.unh.edu Christopher Amato College of Computer and Information Science Northeastern University Boston, MA 02115 camato@ccs.neu.edu
Pseudocode Yes Algorithm 1: The G-DICE Algorithm Algorithm 2: The COG-DICE Algorithm
Open Source Code No The paper does not provide any concrete access information for source code.
Open Datasets No The paper describes two problem domains (tagging simulator and recycling robots problem) but does not provide concrete access information (links, DOIs, repositories, or specific dataset citations) for publicly available or open datasets.
Dataset Splits No The paper describes learning policies within simulated environments and uses Monte Carlo evaluation, but it does not specify train/validation/test dataset splits as these are not relevant to the methodology.
Hardware Specification No The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments.
Software Dependencies No The paper mentions using GMAA*-ICE but does not provide specific version numbers for any software dependencies.
Experiment Setup Yes For all tagging problems we set the input parameters as: Nn = 3, Ni = 500, Nx = 2000, Nk = 50, Nt = 3, α = 0.1. We used the same parameters for G-DICE as we did in the tagging simulation problem with exception to Nk where we used 25. COG-DICE used the same parameters as G-DICE and Nt was set to 2 so that the boundaries between different battery states would be easily identifiable.