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..
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
Authors: Dongseok Shim, Seungjae Lee, H. Jin Kim
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate several experiments on the 3-dimensional environments to explore the effectiveness of SNe RL compared to existing state-of-the-art RL algorithms both in model-free and model-based settings. |
| Researcher Affiliation | Academia | 1Interdisciplinary Program in AI, Seoul National University 2Aerospace Engineering, Seoul National University 3ASRI, AIIS, Seoul National University. Correspondence to: H. Jin Kim <EMAIL>. |
| Pseudocode | Yes | A.3. Pseudo-code |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing the source code for the described methodology or provide a direct link to a code repository. |
| Open Datasets | Yes | We refer to Meta-world (Yu et al., 2020) for more details including the reward function and the range of the random positions. |
| Dataset Splits | No | The paper describes the total dataset size and collection method ("14400 scenes", "Meta-world (Yu et al., 2020)"), but does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined split references for reproduction). |
| Hardware Specification | Yes | Stage 1 (pre-training encoder) in our experiments has been performed using a single NVIDIA RTX A6000 and AMD Ryzen 2950X, and stage 2 (RL downstream tasks) has been performed using an NVIDIA RTX A5000 and AMD Ryzen 2950X. |
| Software Dependencies | No | The paper mentions 'Py Torch-like pseudo-code' and 'Re LU' but does not provide specific version numbers for software dependencies like PyTorch or other libraries used in the experiments. |
| Experiment Setup | Yes | Table 1. Hyperparameters for pre-training multi-view encoder; Table 2. Hyperparameters for SAC (for SNe RL and baselines); Table 3. Hyperparameters for Dreamer (for SNe RL and baselines) |