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[2602.15367] CDRL: A Reinforcement Learning Framework Inspired by Cerebellar Circuits and Dendritic Computational Strategies
Summary
The paper presents CDRL, a reinforcement learning framework inspired by cerebellar circuits, aiming to enhance sample efficiency and robustness in decision-making tasks.
Why It Matters
This research addresses critical limitations in reinforcement learning, such as low sample efficiency and poor generalization, by introducing biologically inspired architectural principles. It highlights the potential of leveraging neuroscience to improve AI systems, which could lead to more effective learning algorithms in complex environments.
Key Takeaways
- CDRL incorporates cerebellar structural principles to improve RL performance.
- The framework enhances sample efficiency, robustness, and generalization.
- Dendritic modulation plays a key role in optimizing RL architectures.
- Sensitivity analysis indicates potential for constrained model parameters.
- Biologically inspired designs can serve as effective inductive biases for RL.
Computer Science > Machine Learning arXiv:2602.15367 (cs) [Submitted on 17 Feb 2026] Title:CDRL: A Reinforcement Learning Framework Inspired by Cerebellar Circuits and Dendritic Computational Strategies Authors:Sibo Zhang, Rui Jing, Liangfu Lv, Jian Zhang, Yunliang Zang View a PDF of the paper titled CDRL: A Reinforcement Learning Framework Inspired by Cerebellar Circuits and Dendritic Computational Strategies, by Sibo Zhang and 4 other authors View PDF HTML (experimental) Abstract:Reinforcement learning (RL) has achieved notable performance in high-dimensional sequential decision-making tasks, yet remains limited by low sample efficiency, sensitivity to noise, and weak generalization under partial observability. Most existing approaches address these issues primarily through optimization strategies, while the role of architectural priors in shaping representation learning and decision dynamics is less explored. Inspired by structural principles of the cerebellum, we propose a biologically grounded RL architecture that incorporate large expansion, sparse connectivity, sparse activation, and dendritic-level modulation. Experiments on noisy, high-dimensional RL benchmarks show that both the cerebellar architecture and dendritic modulation consistently improve sample efficiency, robustness, and generalization compared to conventional designs. Sensitivity analysis of architectural parameters suggests that cerebellum-inspired structures can offer optimized performance for RL wi...