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..
Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes
Authors: Chao Qu, Xiaoyu Tan, Siqiao Xue, Xiaoming Shi, James Zhang, Hongyuan Mei
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate our algorithm SEDRL comprehensively in the synthetic simulation and experiments with real data such as smart broadcasting and improving the engagement of the social media platform. |
| Researcher Affiliation | Collaboration | 1 Ant Group, Hangzhou, China. 2 Toyota Technological Institute at Chicago, Chicago, IL, United States. |
| Pseudocode | Yes | The pseducode of the algorithm is deferred to Appendix. |
| Open Source Code | Yes | We release our code at https://github.com/William BUG/Event driven rl/tree/main |
| Open Datasets | Yes | In particular, we use Retweet dataset (Zhao et al. 2015) to learn a model of follower. We use the data gathered from Stack-Overflow (Leskovec and Krevl 2014) to learn a feedback model of the users. |
| Dataset Splits | No | The paper describes the datasets used and how some parameters are set for the simulation environment, but it does not provide specific train/validation/test dataset split percentages or counts for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, or cloud computing resources) used for running the experiments. |
| Software Dependencies | No | The paper mentions software tools and libraries like PyTorch and model architectures such as LSTM and Transformer, but it does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Other details such as the hyperparameter tunnings in SEDRL and baselines are in Appendix. |