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 [1].
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. |