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..
Off-Policy Actor-Critic with Shared Experience Replay
Authors: Simon Schmitt, Matteo Hessel, Karen Simonyan
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide extensive empirical validation of the proposed solutions on DMLab-30 and further show the benefits of this setup in two training regimes for Atari |
| Researcher Affiliation | Industry | 1Deep Mind. Correspondence to: Simon Schmitt <EMAIL>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We provide extensive empirical validation of the proposed solutions on DMLab-30 and further show the benefits of this setup in two training regimes for Atari. ... As a result, we present state-of-the-art data efficiency in Section 5 in terms of median human normalized performance across 57 Atari games (Bellemare et al., 2013), as well as improved learning efficiency on DMLab30 (Beattie et al., 2016) |
| Dataset Splits | No | The paper discusses training regimes and evaluation metrics (e.g., median score across tasks) but does not provide specific training/validation/test dataset splits or their sizes. |
| Hardware Specification | 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 | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | Following (Xu et al., 2018) we use a discount of 0.995. Motivated by recent work by (Kaiser et al., 2019), we use the IMPALA deep network and increased the number of channels 4 . We use 96% replay data per batch. Differently from (Espeholt et al., 2018), we do not use gradient clipping by norm (Pascanu et al., 2012). Updates are computed on mini-batches of 32 (regular) and 128 (replay) trajectories, each corresponding to 19 steps in the environment. |