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..
Optimistic Policy Optimization with Bandit Feedback
Authors: Lior Shani, Yonathan Efroni, Aviv Rosenberg, Shie Mannor
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For this setting, we propose an optimistic policy optimization algorithm for which we establish O(S2AH4K) regret for stochastic rewards. Furthermore, we prove O(S2AH4K2/3) regret for adversarial rewards. |
| Researcher Affiliation | Academia | 1Technion Israel Institute of Technology, Haifa, Israel 2Tel Aviv University, Tel Aviv, Israel. |
| Pseudocode | Yes | Algorithm 1 POMD with Known Model; Algorithm 2 Optimistic POMD for Stochastic MDPs; Algorithm 3 Optimistic POMD for Adversarial MDPs |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | This paper is theoretical and focuses on algorithm design and proofs, rather than conducting empirical experiments on datasets. Therefore, no information about public datasets is provided. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments or dataset usage, so there are no dataset split details for validation. |
| Hardware Specification | No | This paper is theoretical and does not report on empirical experiments; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and focuses on algorithm design and proofs, without mentioning any specific software dependencies or version numbers. |
| Experiment Setup | No | This paper is theoretical and does not report on empirical experiments; therefore, no experimental setup details such as hyperparameters or training configurations are provided. |