SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
Authors: Dongseok Shim, Seungjae Lee, H. Jin Kim
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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 <hjinkim@snu.ac.kr>. |
| 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) |