A Max-Min Entropy Framework for Reinforcement Learning
Authors: Seungyul Han, Youngchul Sung
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results show that the proposed algorithm yields drastic performance improvement over the current state-of-the-art RL algorithms. and 6 Experiments We provide numerical results to show the performance of the proposed MME and DE-MME in pure exploration and various control tasks. |
| Researcher Affiliation | Academia | Seungyul Han Graduate School of Artificial Intelligence UNIST Ulsan, South Korea 44919 syhan@unist.ac.kr Youngchul Sung School of Electrical Engineering KAIST Daejeon, South Korea 34141 ycsung@kaist.ac.kr |
| Pseudocode | Yes | The detailed implementation and algorithm of MME are provided in Appendix A. |
| Open Source Code | Yes | We provide source code for the proposed method at http://github.com/seungyulhan/mme/ that requires Python Tensorflow. |
| Open Datasets | Yes | Sparse Mujoco [27, 35] is a sparse version of Mujoco [52] in Open AI Gym [8] |
| Dataset Splits | No | No explicit training/validation/test dataset splits (e.g., percentages or sample counts) are mentioned. The paper discusses using random seeds for averaging results over multiple runs. |
| Hardware Specification | Yes | All the algorithms are run on a machine with Intel(R) Core(TM) i7-4770K CPU @ 3.50GHz and an NVIDIA TITAN Xp GPU. |
| Software Dependencies | Yes | We used TensorFlow 1.15.0 and Python 3.6.9 for our implementation. |
| Experiment Setup | Yes | Detailed experimental setup is provided in Appendix B. |