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..
Reinforcement Learning with History Dependent Dynamic Contexts
Authors: Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of our approach on a recommendation task (using Movie Lens data) where user behavior dynamics evolve in response to recommendations. To evaluate the effectiveness of DCZero, we develop a movie recommendation environment based on the Movie Lens dataset (Harper and Konstan, 2015). |
| Researcher Affiliation | Collaboration | 1Google Research 2CREST, ENSAE. |
| Pseudocode | Yes | Algorithm 1 LDC-UCB; Algorithm 2 Tractable LDC-UCB; Algorithm 3 DCZero |
| Open Source Code | No | The paper does not include any explicit statement about providing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | To evaluate the effectiveness of DCZero, we develop a movie recommendation environment based on the Movie Lens dataset (Harper and Konstan, 2015). |
| Dataset Splits | No | All experiments used a horizon of H = 300, M = 6 user classes, A = 6 slate items (changing every reset), and a user embedding dimension of d = 20. The paper mentions dataset usage but does not provide details on train/validation/test splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions building on "Mu Zero" but does not provide specific software dependencies or their version numbers (e.g., Python, library versions). |
| Experiment Setup | Yes | All experiments used a horizon of H = 300, M = 6 user classes, A = 6 slate items (changing every reset), and a user embedding dimension of d = 20. We used default parameters for Mu Zero and applied the same parameters to DCZero. We also vary the values of α on the Attraction Env. |