Deep Reinforcement Learning for Multi-Drug Therapy Optimization in Rare and Refractory Cancers

Abstract

Applies deep reinforcement learning to optimize multi-drug therapy regimens for rare and refractory cancers.

This work intersects with research areas including deep reinforcement learning, drug optimization, rare cancers, refractory cancers, multi-drug therapy, AI oncology, and precision medicine. It is part of the broader research portfolio of Nitiraj V. Kulkarni in the domain of peer-reviewed academic publishing.

Keywords & Topics

deep reinforcement learning, drug optimization, rare cancers, refractory cancers, multi-drug therapy, AI oncology, precision medicine

How to Cite

APA

Nitiraj V. Kulkarni et al. (2026). Deep Reinforcement Learning for Multi-Drug Therapy Optimization in Rare and Refractory Cancers. International Conference on Sustainable Innovation with AI and ML, Pages 195–205, Atlantis Press. Atlantis Press.

IEEE

Nitiraj V. Kulkarni et al., "Deep Reinforcement Learning for Multi-Drug Therapy Optimization in Rare and Refractory Cancers," International Conference on Sustainable Innovation with AI and ML, Pages 195–205, Atlantis Press, 2026.

BibTeX

@article{2026_deep_rl_multi_drug_rare_cancers,
  title={Deep Reinforcement Learning for Multi-Drug Therapy Optimization in Rare and Refractory Cancers},
  author={Nitiraj V. Kulkarni et al.},
  journal={International Conference on Sustainable Innovation with AI and ML},
  year={2026},
  publisher={Atlantis Press}
}

Identifiers & Links

Last updated: 2026-01-01

About the Author

Nitiraj V. Kulkarni is an AI safety and cybersecurity researcher based in Pune, India, with 35+ peer-reviewed publications, 5 patents, 6 copyright registrations, and 15,000+ open datasets published on Kaggle and Zenodo. He serves as a peer reviewer for 11+ international journals and conferences. Read full profile.