TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments

10 September 2020

Papers citing "TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments"

5 / 5 papers shown

Title
Policy-to-Language: Train LLMs to Explain Decisions with Flow-Matching Generated Rewards Xinyi Yang Liang Zeng Heng Dong Chao Yu Xiaojun Wu H. Yang Yu Wang Milind Tambe Tonghan Wang 83 2 0 18 Feb 2025
On Generating Explanations for Reinforcement Learning Policies: An Empirical Study Mikihisa Yuasa Huy T. Tran R. Sreenivas FAtt LRM 74 1 0 29 Sep 2023
Decisions that Explain Themselves: A User-Centric Deep Reinforcement Learning Explanation System Xiaoran Wu Zihan Yan Chongjie Zhang Tongshuang Wu 26 1 0 01 Dec 2022
A Survey of Explainable Reinforcement Learning Stephanie Milani Nicholay Topin Manuela Veloso Fei Fang XAI LRM 50 53 0 17 Feb 2022
Tree-based Focused Web Crawling with Reinforcement Learning Andreas Kontogiannis Dimitrios Kelesis Vasilis Pollatos George Giannakopoulos Georgios Paliouras 29 2 0 12 Dec 2021