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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
State Entropy Maximization with Random Encoders for Efficient Exploration
Authors: Younggyo Seo, Lili Chen, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 19,196 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,571 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 | Our experiments show that RE3 significantly improves the sample-efficiency of both model-free and model-based RL methods on locomotion and navigation tasks from Deep Mind Control Suite and Mini Grid benchmarks. |
| Researcher Affiliation | Collaboration | 1KAIST 2UC Berkeley 3University of Michigan 4LG AI Research. |
| Pseudocode | Yes | Algorithm 1 RE3: Off-policy RL version |
| Open Source Code | Yes | Source code is available at https://sites.google.com/view/re3-rl. |
| Open Datasets | Yes | RE3 significantly improves the sample-efficiency of both model-free and model-based RL methods on widely used Deep Mind Control Suite (Tassa et al., 2020), Mini Grid (Chevalier-Boisvert et al., 2018), and Atari (Bellemare et al., 2013) benchmarks. |
| Dataset Splits | No | The paper mentions using well-known benchmarks but does not specify explicit training/validation/test splits (e.g., percentages or sample counts) for reproducibility within the paper's text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9') needed to replicate the experiments. |
| Experiment Setup | Yes | As for the newly introduced hyperparameters, we use k = 3, β0 {0.05, 0.25}, and ρ {0.0, 0.00001, 0.000025}. We provide more details in Appendix A. |