Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning
No confident code implementation found for this paper.
No confident code implementation found for this paper.
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
Mastering Task Arithmetic: τJp as a Key Indicator for Weight Disentanglement
SatBird: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data
StressID: a Multimodal Dataset for Stress Identification
Enhancing Decision Analysis with a Large Language Model: pyDecision a Comprehensive Library of MCDA Methods in Python
STREAMER: Streaming Representation Learning and Event Segmentation in a Hierarchical Manner
Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface
@article{zamboni2026distributional,
title = {Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning},
author = {Riccardo Zamboni and A. Metelli and Marcello Restelli},
year = {2026},
doi = {10.52202/075280-0575},
url = {https://doi.org/10.52202/075280-0575},
journal = {NEURIPS 2023 2023}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/2f10edde08e33a8d08ce63adf7ab488b95f900f1)