Insect-inspired modular architectures as inductive biases for reinforcement learning
Most reinforcement-learning (RL) controllers used in continuous control are architecturally centralized: observations are compressed into a single latent state from which both value estimates and actions are produced. Biological control systems are often organized differently. Insects, in particular, coordinate navigation, heading stabilization, memory, and context-dependent action selection through distributed circuits rather than a single monolithic controller. Motivated by this contrast, we study an RL policy architecture that decomposes control into interacting modules for sensory encoding, heading representation, sparse associative memory, recurrent command generation, and local moto...
| Authors | Anne E. Staples |
|---|---|
| Primary category | cs.LG |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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