CDRL: A Reinforcement Learning Framework Inspired by Cerebellar Circuits and Dendritic Computational Strategies
arXiv:2602.15367v1 Announce Type: new 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-in
arXiv:2602.15367v1 Announce Type: new 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 with constrained model parameters. Overall, our work underscores the value of cerebellar structural priors as effective inductive biases for RL.
Executive Summary
This article introduces CDRL, a reinforcement learning framework inspired by cerebellar circuits and dendritic computational strategies. The proposed architecture incorporates structural principles of the cerebellum, including large expansion, sparse connectivity, sparse activation, and dendritic-level modulation. Experiments on noisy, high-dimensional RL benchmarks demonstrate improved sample efficiency, robustness, and generalization compared to conventional designs. The study underscores the value of cerebellar structural priors as effective inductive biases for RL. The findings have significant implications for the development of more efficient and robust reinforcement learning algorithms.
Key Points
- ▸ CDRL is a biologically grounded RL architecture inspired by cerebellar circuits and dendritic computational strategies.
- ▸ The proposed architecture improves sample efficiency, robustness, and generalization in noisy, high-dimensional RL benchmarks.
- ▸ Sensitivity analysis of architectural parameters suggests that cerebellum-inspired structures can offer optimized performance for RL with constrained model parameters.
Merits
Strengths in Biological Grounding
The study's use of cerebellar structural priors as inductive biases for RL provides a novel and biologically grounded approach to reinforcement learning.
Improved Sample Efficiency
The proposed architecture demonstrates improved sample efficiency in noisy, high-dimensional RL benchmarks, a significant challenge in current RL research.
Robustness and Generalization
The study shows that CDRL consistently improves robustness and generalization compared to conventional designs, making it a promising approach for RL applications.
Demerits
Limited Generalizability
The study's focus on cerebellar-inspired structures may limit the generalizability of the findings to other types of neural networks or RL algorithms.
Computational Complexity
The proposed architecture may introduce additional computational complexity, which could be a limitation in resource-constrained environments.
Expert Commentary
The study's use of cerebellar-inspired structures as inductive biases for RL provides a novel and biologically grounded approach to reinforcement learning. While the study's findings are promising, the limitations of the approach, including potential computational complexity and limited generalizability, must be carefully considered. The study's implications for the development of more efficient and robust RL algorithms are significant, and further research is needed to fully explore the potential of CDRL.
Recommendations
- ✓ Future research should focus on exploring the generalizability of CDRL to other types of neural networks and RL algorithms.
- ✓ Investigations into the computational complexity of CDRL and potential optimization strategies to mitigate this limitation are essential.
