State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding
Authors: Devleena Das, Sonia Chernova, Been Kim
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental validations, in Connect 4 and Lunar Lander, demonstrate the success of S2E in providing a dual-benefit, successfully informing reward shaping and improving agent learning rate, as well as significantly improving end user task performance at deployment time. |
| Researcher Affiliation | Collaboration | Devleena Das School of Interactive Computing Georgia Institute of Technology ddas41@gatech.edu Sonia Chernova School of Interactive Computing Georgia Institute of Technology chernova@gatech.edu Been Kim Google Research beenkim@google.com |
| Pseudocode | No | The paper includes architectural diagrams and descriptions of its framework, but it does not contain explicit pseudocode blocks or algorithms labeled as such. |
| Open Source Code | Yes | See link to code in Appendix F. https://anonymous.4open.science/r/S2E/README.md |
| Open Datasets | Yes | Lunar Lander is a trajectory optimization problem in which the lander must land on a landing pad. We utilize Lunar Lander-v2 from Open AI Gym [4]. |
| Dataset Splits | Yes | To train and evaluate MC4 and MLL, we utilize a 60%-20%-20% train-valid-test split on DC4 and DLL (see total dataset size in Appendix C.1). |
| Hardware Specification | Yes | To train MC4 and MLL, we utilize a desktop computer with a NVIDIA GTX 1060 6GB GPU and an Intel i7 processor. |
| Software Dependencies | No | The paper mentions software like 'Lunar Lander-v2 from Open AI Gym [4]' and 'Mu Zero [52]', but it does not provide specific version numbers for software dependencies such as libraries or programming languages. |
| Experiment Setup | Yes | The models are trained with learning rate of 0.001, batch size of 128, Adam Optimizer, and trained with 10 epochs. |