Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Provable Representation Learning for Imitation with Contrastive Fourier Features
Authors: Ofir Nachum, Mengjiao Yang
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,701 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,626 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both tabular environments and high-dimensional Atari games provide quantitative evidence for the practical benefits of our proposed objective. |
| Researcher Affiliation | Industry | Ofir Nachum Google Brain EMAIL Mengjiao Yang Google Brain EMAIL |
| Pseudocode | Yes | see Appendix A for pseudocode. |
| Open Source Code | Yes | Find experimental code at https://github.com/google-research/google-research/tree/ master/rl_repr. |
| Open Datasets | Yes | We take for the offline dataset the DQN Replay Dataset [7], which for each game provides 50M steps collected during DQN training. |
| Dataset Splits | No | The paper describes the datasets used for training and evaluation but does not specify explicit train/validation/test splits for reproducibility. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, or memory) used for running experiments. |
| Software Dependencies | No | The paper mentions extending implementations from 'Dopamine [14]' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | In our implementation we choose αR = 1, αT = (1 γ) 1 to roughly match the coefficients of the bounds in Theorems 2 and 3. |