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..
Provably Efficient Neural GTD for Off-Policy Learning
Authors: Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform preliminary experiments to support the above theories on a toy example of off-policy learning. ... In Fig. 1, we compare the average MSBE against the number of neurons m, using a 2-layer, Re LU NN with random initialization according to H1, after T = 3 * 10^5 iterations of neural GTD and neural TD [Cai et al., 2019] run with Markovian samples [cf. Algorithm 1], from 10 independent runs of state/action. |
| Researcher Affiliation | Academia | Hoi-To Wai The Chinese University of Hong Kong ... Zhuoran Yang Princeton University ... Zhaoran Wang Northwestern University ... Mingyi Hong University of Minnesota |
| Pseudocode | Yes | Algorithm 1 Neural GTD algorithms for MSBE |
| Open Source Code | No | The paper does not provide any information about open-source code for the methodology. |
| Open Datasets | No | We consider an MDP taken from the Garnet class with |S| = 500 states, |A| = 5 possible actions per state with uniformly distributed rewards, and the discount factor is gamma = 0.9. We generate two random policies with the same support as the behavior/target policies, respectively. This describes a simulated environment setup, but not a publicly available dataset with a link or formal citation. |
| Dataset Splits | No | The paper describes a simulation environment and runs for a fixed number of iterations (T = 3 * 10^5) and independent runs (10), but does not explicitly mention training, validation, or test dataset splits or percentages. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We consider an MDP taken from the Garnet class with |S| = 500 states, |A| = 5 possible actions per state with uniformly distributed rewards, and the discount factor is gamma = 0.9. We generate two random policies with the same support as the behavior/target policies, respectively. In Fig. 1, we compare the average MSBE against the number of neurons m, using a 2-layer, Re LU NN with random initialization according to H1, after T = 3 * 10^5 iterations of neural GTD and neural TD [Cai et al., 2019] run with Markovian samples [cf. Algorithm 1], from 10 independent runs of state/action. |