SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
Authors: Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew Johnson, Sergey Levine
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on a range of robotics tasks, including manipulation with a real-world robotic arm directly from images. We find that our method produces substantially better final performance than other model-based RL methods while being significantly more efficient than model-free RL. |
| Researcher Affiliation | Collaboration | 1University of California, Berkeley 2University of California, San Diego 3Google. |
| Pseudocode | Yes | Algorithm 1 SOLAR |
| Open Source Code | Yes | Our open-source implementation of SOLAR is available at https://github.com/sharadmv/parasol. |
| Open Datasets | Yes | We experiment with the reacher environment from Open AI Gym (Brockman et al., 2016) |
| Dataset Splits | No | The paper does not explicitly provide specific train/validation/test dataset splits (e.g., percentages or counts) or reference predefined splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Details regarding task setup and training hyperparameters are provided in Appendix E. |