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 [1].
Robustly Learning Composable Options in Deep Reinforcement Learning
Authors: Akhil Bagaria, Jason Senthil, Matthew Slivinski, George Konidaris
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test these improvements on four sparse-reward maze navigation tasks involving a simulated quadrupedal robot. Each method successively improves the robustness of a baseline skill discovery method, substantially outperforming state-of-the-art ο¬at and hierarchical methods. |
| Researcher Affiliation | Academia | Department of Computer Science, Brown University EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithms but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Video, code and appendix can be found at https://sites.google. com/brown.edu/robustly-composing-options |
| Open Datasets | Yes | To answer these questions, we use a test-bed comprising four continuous-control maze-navigation tasks (shown in Figure 3) involving an ant robot simulated using Mu Jo Co [Todorov et al., 2012; Duan et al., 2016; Fu et al., 2020]. |
| Dataset Splits | No | The paper describes training and evaluation but does not specify explicit training/validation/test dataset splits with percentages, sample counts, or specific predefined split citations. |
| Hardware Specification | No | The paper mentions using 'computational resources and services at the Center for Computation and Visualization, Brown University' but does not provide specific hardware details such as GPU/CPU models, memory, or processor types. |
| Software Dependencies | No | The paper mentions software like TD3, HER, MuJoCo, and SVM, but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | After both algorithm variants were trained for 1000 episodes, they were evaluated on how many steps it would take them to reach the goal when starting in the four corners of the domain {( 9, 9)}. |