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..
Latent exploration for Reinforcement Learning
Authors: Alberto Silvio Chiappa, Alessandro Marin Vargas, Ann Huang, Alexander Mathis
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With extensive experiments, we show that Lattice can replace standard unstructured exploration [2, 5] and time-only-correlated exploration (g SDE) [8] in off-policy (SAC) and on-policy (PPO) RL algorithms, and improve performance in complex motor control tasks. Importantly, we demonstrate that Lattice-SAC is competitive in standard benchmarks for continuous control, such as the locomotion environments of Py Bullet [20]. We benchmarked Lattice on standard locomotion tasks [47, 6, 16, 48 50] in Py Bullet [20], as well as musculoskeletal control tasks of Myo Suite [18] built in Mu Jo Co [31]. All the results are averaged across 5 random seeds. |
| Researcher Affiliation | Academia | Alberto Silvio Chiappa École Polytechnique Fédérale de Lausanne (EPFL) EMAIL Alessandro Marin Vargas EPFL EMAIL Ann Zixiang Huang Mila, EPFL EMAIL Alexander Mathis EPFL EMAIL |
| Pseudocode | Yes | Algorithm 1 Standard (e.g., PPO, SAC) and Algorithm 2 Lattice are presented on page 4. |
| Open Source Code | Yes | The code is available at: https://github.com/amathislab/lattice. |
| Open Datasets | Yes | We benchmarked Lattice on standard locomotion tasks [47, 6, 16, 48 50] in Py Bullet [20], as well as musculoskeletal control tasks of Myo Suite [18] built in Mu Jo Co [31]. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits with percentages or sample counts. It refers to training in environments and averaging results over random seeds, which is common in RL, but not explicit dataset splits. |
| Hardware Specification | No | The training was run on a GPU cluster, for a total of approximately 10,000 GPU-hours. No specific GPU models, CPU types, or detailed cluster specifications are provided beyond 'GPU cluster'. |
| Software Dependencies | No | We implemented Lattice as an extension of g SDE in the RL library Stable Baselines 3 [45]. While Stable Baselines 3 is mentioned, no specific version number is provided for it or any other software dependency. |
| Experiment Setup | Yes | We used the same network architecture and hyperparameters for SAC specified in [46] for all the environments (see Appendix A.7). Tables T3, T4, and T5 in Appendix A.7 provide detailed hyperparameters for SAC, PPO, g SDE, and Lattice for various tasks. |