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..
Explicit Explore-Exploit Algorithms in Continuous State Spaces
Authors: Mikael Henaff
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then present a practical version of the algorithm using neural networks, and demonstrate its performance and sample efficiency empirically on several problems with large or continuous state spaces. (Abstract & Introduction)We now give empirical results for the Neural-E3 algorithm described in Section 4. (Section 6) |
| Researcher Affiliation | Industry | Mikael Henaff Microsoft Research EMAIL |
| Pseudocode | Yes | Algorithm 1 (M, Π, n, ϵ, φ) and Algorithm 2 Update Model Set(Mt, R, φ) |
| Open Source Code | Yes | See Appendix C for experimental details and https://github.com/mbhenaff/neural-e3 for source code. |
| Open Datasets | Yes | We begin with a set of experiments on the stochastic combination lock environment described in [14], We next evaluated our approach on a maze environment, which is a modified version of the Collect domain [35], Mountain Car [32], Acrobot [50]. For the Mountain Car and Acrobot experiments, we used the OpenAI Gym [6] implementations. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits. It describes running experiments in simulation environments over a number of episodes, rather than using fixed datasets with explicit splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Open AI Baselines implementation [13]' and 'Adam [25]' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We used Adam [25] for all neural network training with a learning rate of 1e-4 and a batch size of 32. (Appendix C) |