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..
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
Authors: Michael Laskin, Aravind Srinivas, Pieter Abbeel
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | CURL outperforms prior pixel-based methods, both model-based and model-free, on complex tasks in the Deep Mind Control Suite and Atari Games showing 1.9x and 1.2x performance gains at the 100K environment and interaction steps benchmarks respectively. |
| Researcher Affiliation | Academia | Michael Laskin 1 Aravind Srinivas 1 Pieter Abbeel 1 1University of California, Berkeley, BAIR. Correspondence to: Michael Laskin, Aravind Srinivas, <mlaskin, aravind EMAIL>. |
| Pseudocode | Yes | 4.7. CURL Contrastive Learning Pseudocode (Py Torch-like) |
| Open Source Code | Yes | Our code is open-sourced and available at https://www. github.com/Misha Laskin/curl. |
| Open Datasets | Yes | We benchmark for sample-ef๏ฌciency on the DMControl suite (Tassa et al., 2018) and Atari Games benchmarks (Bellemare et al., 2013). |
| Dataset Splits | No | The paper describes training and evaluation within reinforcement learning environments (DMControl, Atari Games) where data is collected dynamically via agent interaction and stored in a replay buffer. It does not provide explicit training/validation/test dataset splits with percentages or counts as would be found in supervised learning. |
| Hardware Specification | No | The paper mentions receiving 'Google TFRC for cloud credits' in the acknowledgements, but does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch-like' in the pseudocode section but does not provide specific version numbers for PyTorch or any other software dependencies, such as libraries or environments, used in the experiments. |
| Experiment Setup | Yes | The paper provides specific experimental setup details such as the momentum parameter for target encoding ('m: momentum, e.g. 0.95'), and details on data augmentation ('Our aspect ratio for cropping is 0.84, i.e, we crop a 84 84 image from a 100 100 simulation-rendered image.'). |