A Family of Robust Stochastic Operators for Reinforcement Learning
Authors: Yingdong Lu, Mark Squillante, Chai Wah Wu
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results illustrate the strong benefits of our robust stochastic operators, significantly outperforming the classical Bellman and recently proposed operators. |
| Researcher Affiliation | Industry | Yingdong Lu, Mark S. Squillante, Chai Wah Wu Mathematical Sciences IBM Research Yorktown Heights, NY 10598, USA {yingdong, mss, cwwu}@us.ibm.com |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'Appendix C of the supplement provides the corresponding python code modifications used in our experiments,' but does not explicitly state that the source code for the general methodology described in the paper is released as open-source. |
| Open Datasets | Yes | We conduct various experiments across several well-known problems using the Open AI Gym framework [10], namely Acrobot, Mountain Car, Cart Pole and Lunar Lander. |
| Dataset Splits | No | The paper does not provide explicit training/validation/test dataset splits (e.g., percentages, counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions using the 'Open AI Gym framework' and 'python code modifications' but does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions using 'default parameter settings' and experimenting over a 'wide range of values for ϵ' but does not explicitly list specific hyperparameter values or detailed training configurations for reproduction. |