Efficient and Scalable Reinforcement Learning

Counteractive RL: Rethinking Core Principles for Efficient and Scalable Deep Reinforcement Learning

Conference on Neural Information Processing Systems, NeurIPS 2025

✨ Spotlight Presentation ✨

This paper introduces a fundamental paradigm for reinforcement learning that accelarest learning. Our approach centers on solely reconstituting and conceptually shifting the core principles of learning and as a result increases the information gained from the environment interactions of the policy in a given MDP without adding computational complexity. Our analysis and method provide a theoretical basis for efficient, effective, scalable and accelerated reinforcement learning.

Citation

@article{korkmaz2025counteractive,
  title={Counteractive RL: Rethinking Core Principles for Efficient and Scalable Deep Reinforcement Learning},
  author={Korkmaz, Ezgi},
  journal={Conference on Neural Information Processing Systems, NeurIPS},
  year={2025},
  url={https://openreview.net/pdf?id=qaHrpITIvB}
}