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..
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
Authors: Scott Fujimoto, David Meger, Doina Precup
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate both LAP and PAL on the suite of Mu Jo Co environments [3] and a set of Atari games [4]. Across both domains, we find both of our methods outperform the vanilla algorithms they modify. In the Mu Jo Co domain, we find significant gains over the state-of-the-art in the hardest task, Humanoid. All code is open-sourced (https://github.com/sfujim/LAP-PAL). |
| Researcher Affiliation | Academia | Scott Fujimoto, David Meger, Doina Precup Mila, Mc Gill University EMAIL |
| Pseudocode | No | The paper describes the algorithms mathematically but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All code is open-sourced (https://github.com/sfujim/LAP-PAL). |
| Open Datasets | Yes | We evaluate the benefits of LAP and PAL on the standard suite of Mu Jo Co [3] continuous control tasks as well as a subset of Atari games, both interfaced through Open AI gym [41]. |
| Dataset Splits | No | The paper does not provide explicit details about training, validation, or test dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or detailed computer specifications used for running experiments. |
| Software Dependencies | No | The paper mentions software like Open AI gym and specific algorithms, but does not provide version numbers for any software dependencies. |
| Experiment Setup | No | A complete list of hyper-parameters and experimental details are provided in the supplementary material. |