# CDRL: A Reinforcement Learning Framework Inspired by Cerebellar Circuits and Dendritic Computational Strategies

arXiv: https://arxiv.org/abs/2602.15367 · HTML: https://mlsanity.com/paper/2602.15367/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 17 Feb 2026.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 15 of 15)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations](https://mlsanity.com/paper/2003.08938/) (2020)
- [Action Robust Reinforcement Learning and Applications in Continuous Control](https://mlsanity.com/paper/1901.09184/) (2019)
- [The State of Sparse Training in Deep Reinforcement Learning](https://mlsanity.com/paper/2206.10369/) (2022)
- [TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning](https://mlsanity.com/paper/2306.13229/) (2023)
- [A Survey on Causal Reinforcement Learning](https://mlsanity.com/paper/2302.05209/) (2023)
- [FlyLoRA: Boosting Task Decoupling and Parameter Efficiency via Implicit Rank-Wise Mixture-of-Experts](https://mlsanity.com/paper/2510.08396/) (2025)
- [Policy Regularization with Dataset Constraint for Offline Reinforcement Learning](https://mlsanity.com/paper/2306.06569/) (2023)
- [Been There, Done That: Meta-Learning with Episodic Recall](https://mlsanity.com/paper/1805.09692/) (2018)
- [Parseval Regularization for Continual Reinforcement Learning](https://mlsanity.com/paper/2412.07224/) (2024)
- [Structural features of the fly olfactory circuit mitigate the stability-plasticity dilemma in continual learning](https://mlsanity.com/paper/2502.01427/) (2025)
- [Fly-CL: A Fly-Inspired Framework for Enhancing Efficient Decorrelation and Reduced Training Time in Pre-trained Model-based Continual Representation Learning](https://mlsanity.com/paper/2510.16877/) (2025)
- [Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning](https://mlsanity.com/paper/2404.03708/) (2024)
- [DRPC: Distributed Reinforcement Learning Approach for Scalable Resource Provisioning in Container-based Clusters](https://mlsanity.com/paper/2407.10169/) (2024)

## Abstract (arXiv)

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.