Learning Predictive State Representations From Non-Uniform Sampling
Authors: Yuri Grinberg, Hossein Aboutalebi, Melanie Lyman-Abramovitch, Borja Balle, Doina Precup
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations on both synthetic and real datasets highlight the advantages of the proposed approach.We conducted several experiments on real and simulated environments in order to evaluate our denoising algorithm, in comparison to the standard PSR learning approach. |
| Researcher Affiliation | Collaboration | 1 National Research Council of Canada 2 Mc Gill University, Canada 3 Amazon Research Cambridge, UK |
| Pseudocode | Yes | Algorithm 1: Denoising Algorithm Data: ˆPH,T = U T SV Rm n, step size α , threshold ϵ, reg. parameters λ1, λ2 Let f(Q, R) = i,j || ˆPH,T QR||2 F,W + λ1( k |C| i,j Ck(q T xiryi q T xjryj)2) +λ2 l k=1 || y Yk R:,y R:,jk||2 Result: Rank-k matrix R Initialize: Q = U T S; R = V ; Until: convergence Q = Q + α Qf(Q, R); R = R + α Rf(Q, R); |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper uses synthetic, simulated, and 'real data obtained from the robot cyclically exploring his environment using 24 sensors (Freire et al. 2009)'. While a citation is provided for the origin of the robot data, it refers to a paper describing the data gathering process, not a direct link, DOI, or repository for public access to the dataset itself. |
| Dataset Splits | Yes | Performance was evaluated using 5-fold cross validation, such that each fold represents a new trajectory of the robot. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Fitted-Q iteration (FQI)' and 'Extremely randomized trees' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | In all experiments we set the Frobenious norm weights to be the number of samples used to estimate each entry as a proxy to the inverse variance.The SDM was constructed using histories up to length 3 and tests up to length 2.In all cases, we used rank-15 SDM, as it produced better performance for both the standard and denoising methods.A rank-40 SDM was used to compute PSR parameters, as increasing the rank did not have any notable effect on either of the methods. |