Implementation of reinforcement learning in chemical reaction networks: application to phototaxis as curiosity-driven exploration
arXiv:2606.26168v1 Announce Type: new Abstract: Living systems navigate environments using noisy and incomplete sensory signals. In unicellular algae, phototaxis is often modeled as a mechanistic run--tumble process driven by stimulus--response rules. However, such descriptions overlook how organisms actively sample their environment to reduce sensory ambiguity. From a minimal cognition perspective, we reframe this navigation as a subjective, information-driven sensorimotor process. To this end, we propose a framework linking a Partially Observable Markov Decision Process (POMDP) with biochemi
The paper leverages recent advancements in reinforcement learning and a 'minimal cognition perspective' to reframe biological navigation, reflecting an interdisciplinary convergence in AI research.
This research provides a novel framework for understanding and potentially replicating biological intelligence and adaptive decision-making within engineered systems, moving beyond simple stimulus-response models.
It introduces a POMDP-based framework for modeling how organisms actively sample environments, shifting the paradigm from passive reaction to active, information-driven exploration in artificial and potentially bio-inspired systems.
- · AI researchers
- · Synthetic biology
- · Robotics
- · Biotechnology
Improved understanding and modeling of biological intelligence and adaptive behavior in complex environments.
Development of more robust and autonomous AI agents capable of curiosity-driven exploration and decision-making in real-world scenarios.
Potential for synthetic life forms with emergent cognitive behaviors, blurring the lines between biological and artificial intelligence.
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Read at arXiv cs.LG