Connectome-based Reinforcement Learning for Embodied Neural Circuit Optimization

Reinforcement learning policies are typically represented by generic neural networks, whose architectures are largely independent of biological neural organization. In contrast, animal behavior emerges from efficient, structured neural circuits through continuous interaction with the body and environment. Whether an experimentally grounded connectome can serve directly as a reinforcement learning policy remains largely unexplored. To address this question, we propose Connectome-as-Policy, which formulates a connectome-derived Caenorhabditis elegans salt chemotaxis subcircuit as the actor in a closed-loop embodied learning system. The framework combines a fixed circuit topology, continuous neural dynamics, a simulated worm body, and a two-dimensional salt field. Reinforcement learning optimizes synaptic strengths and neuronal parameters while preserving the predefined connectivity. Across six reinforcement learning algorithms, the connectome policy learns stable chemotactic navigation and concentration regulation. Compared with conventional neural backbones, it achieves competitive performance with substantially fewer trainable parameters while enabling circuit-level interpretability. Robustness analyses reveal smooth behavioral transitions and stable performance under varying concentrations and sensory noise. Additional reinforcement learning benchmarks demonstrate comparable performance across standard control tasks. These results suggest that biological circuit priors offer a compact and interpretable foundation for embodied reinforcement learning.