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..
APS: Active Pretraining with Successor Features
Authors: Hao Liu, Pieter Abbeel
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | When evaluated on the Atari 100k data-efficiency benchmark, our approach significantly outperforms previous methods combining unsupervised pretraining with task-specific finetuning. |
| Researcher Affiliation | Academia | 1University of California, Berkeley, CA, USA. |
| Pseudocode | Yes | Algorithm 1: Training APS |
| Open Source Code | No | No, the paper does not provide a link to open-source code or explicitly state that the code is available. |
| Open Datasets | Yes | We evaluate our approach on the Atari benchmark (Bellemare et al., 2013) where we apply APS to Dr Q (Kostrikov et al., 2020) and test its performance after fine-tuning for 100K supervised environment steps. |
| Dataset Splits | No | No, the paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or test sets. |
| Hardware Specification | No | No, the paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | No, the paper mentions frameworks and prior works it builds upon (e.g., Dr Q, Adam optimizer, pycolab game engine) but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | We largely follow Hansen et al. (2020) for hyperparameters used in our Atari experiments, with the following three exceptions. [...] We use the Adam optimizer (Kingma & Ba, 2015) with an learning rate 0.0001. We use discount factor γ = .99. Standard batch size of 32. ψ is coupled with a target network (Mnih et al., 2015), with an update period of 100 updates. |