Provable Representation Learning for Imitation with Contrastive Fourier Features
Authors: Ofir Nachum, Mengjiao Yang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 ofirnachum@google.com Mengjiao Yang Google Brain sherryy@google.com |
| 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. |