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..
CoinDICE: Off-Policy Confidence Interval Estimation
Authors: Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvari, Dale Schuurmans
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now evaluate the empirical performance of Coin DICE, comparing it to a number of existing confidence interval estimators for OPE based on concentration inequalities. |
| Researcher Affiliation | Collaboration | 1Google Research, Brain Team 2University of Alberta 3Deep Mind |
| Pseudocode | No | The provided text does not contain any structured pseudocode or algorithm blocks, although it refers to "Algorithm 1" in an appendix. |
| Open Source Code | Yes | Open-source code for Coin DICE is available at https://github.com/google-research/dice_rl. |
| Open Datasets | Yes | We use Frozen Lake (Brockman et al., 2016), a highly stochastic gridworld environment, and Taxi (Dietterich, 1998), an environment with a moderate state space of 2 000 elements. ... Lastly, we evaluate Coin DICE on Reacher (Brockman et al., 2016; Todorov et al., 2012), a continuous control environment. |
| Dataset Splits | No | The paper mentions collecting a static dataset and sampling from it, but does not specify explicit training/validation/test splits, percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions). |
| Experiment Setup | Yes | The discount factor is γ = 0.99. The target policy is taken to be a near-optimal one, while the behavior policy is highly suboptimal. The behavior policy in Frozen Lake is the optimal policy with 0.2 white noise... in this setting, we use a one-hidden-layer neural network with ReLU activations. |