JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning
Authors: Zichuan Lin, Junyou Li, Jianing Shi, Deheng Ye, Qiang Fu, Wei Yang
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that Jue Wu-MC significantly improves sample efficiency and outperforms a set of baselines by a large margin. |
| Researcher Affiliation | Industry | Tencent AI Lab, Shenzhen, China |
| Pseudocode | No | The paper states 'The full algorithm is shown in Appendix.' and 'We show the algorithm in Appendix.', but the appendix is not provided in the given text, thus no pseudocode is visible. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We conduct experiments on Mine RL environment [Guss et al., 2019]. |
| Dataset Splits | No | The paper mentions '8 million samples' and '0.5 million training samples', '2.5 million training samples' but does not specify the explicit training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware specifications (e.g., GPU models, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions building upon existing RL algorithms like SQIL, PPO, and DQf D, but it does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper states 'We present the details of our approach as well as the experiment settings in Appendix.' but the appendix content is not provided, thus specific experimental setup details like hyperparameters are not visible in the given text. |