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..
Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy
Authors: Riqiang Gao, Florin-Cristian Ghesu, Simon Arberet, Shahab Basiri, Esa Kuusela, Martin Kraus, Dorin Comaniciu, Ali Kamen
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have conducted experiments on four datasets with four metrics and compared our model with a leading optimization sequencer. |
| Researcher Affiliation | Industry | 1Digital Technology and Innovation, Siemens Healthineers, Princeton NJ, USA 2Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany 3Varian Medical Systems, Siemens Healthineers, Helsinki, Finland. |
| Pseudocode | Yes | Algorithm 1 RLS training |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code of the described methodology. |
| Open Datasets | Yes | The HN site includes three datasets: 1) the HNd set contains 493 patients after quality assurance used for training and test with a train/validation/test split, 2) two external test sites from TCIA: HNe1 (Bejarano et al., 2018) with 31 patients and HNe2 (Grossberg et al., 2020) with 140 patients after filtering. The Pros site is from a public dataset with access permission requirements, including 555 patients after filtering. |
| Dataset Splits | Yes | HNd set contains 493 patients after quality assurance used for training and test with a train/validation/test split, 2) two external test sites from TCIA: HNe1 (Bejarano et al., 2018) with 31 patients and HNe2 (Grossberg et al., 2020) with 140 patients after filtering. The Pros site is from a public dataset with access permission requirements, including 555 patients after filtering. |
| Hardware Specification | Yes | GPU type: NVIDIA RTX A4500 |
| Software Dependencies | Yes | deep learning platform: Py Torch 1.13 |
| Experiment Setup | Yes | RL iterations: 20000 Update epochs: 2 batch_size: 96... discount factor gamma: 0.99... learning rate: 1e-4... optimizer: Adam W |