Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Utility Theory for Sequential Decision Making
Authors: Mehran Shakerinava, Siamak Ravanbakhsh
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We extend these axioms to increasingly structured sequential decision making settings and identify the structure of the corresponding utility functions. In particular, we show that memoryless preferences lead to a utility in the form of a per transition reward and multiplicative factor on the future return. This result motivates a generalization of Markov Decision Processes (MDPs) with this structure on the agent s returns, which we call Affine-Reward MDPs. |
| Researcher Affiliation | Academia | 1School of Computer Science, Mc Gill University, Montreal, Canada 2Mila Quebec AI Institute. |
| Pseudocode | No | The paper is theoretical and focuses on mathematical proofs and axioms. It does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing code, nor does it provide links to source code repositories. |
| Open Datasets | No | The paper is purely theoretical and does not mention any datasets used for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used for computations. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies or versions used for experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe any experiments, hyperparameters, or training configurations. |