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..
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL
Authors: Bogdan Mazoure, Ahmed M Ahmed, R Devon Hjelm, Andrey Kolobov, Patrick MacAlpine
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments1 ablate various components of CTRL and demonstrate that in combination with PPO it achieves better generalization performance on the challenging Procgen benchmark suite (Cobbe et al., 2020). |
| Researcher Affiliation | Collaboration | Bogdan Mazoure EMAIL Mc Gill University, Quebec AI Institute Ahmed M. Ahmed EMAIL Stanford University Patrick Mac Alpine EMAIL Sony AI R Devon Hjelm EMAIL Université de Montréal, Quebec AI Institute, Microsoft Research Andrey Kolobov EMAIL Microsoft Research |
| Pseudocode | Yes | CTRL s pseudocode presented in Algorithm 1 in Appendix 8.1. |
| Open Source Code | Yes | 1Code link: https://github.com/bmazoure/ctrl_public |
| Open Datasets | Yes | We compare CTRL against strong RL baselines: DAAC (Raileanu and Fergus, 2021) the current state-of-the-art on the challenging generalization benchmark suite Procgen (Cobbe et al., 2020) |
| Dataset Splits | No | The paper mentions training on N=200 levels and evaluating on tasks not seen during training (d(T \ TN)), which implies a train/test split, but does not explicitly describe a separate validation split or its proportion/methodology. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions software like 'IMPALA architecture', 'PPO', and 'Adam' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Table 2: Experiments parameters |