# Implementation of reinforcement learning in chemical reaction networks: application to phototaxis as curiosity-driven exploration

arXiv: https://arxiv.org/abs/2606.26168 · HTML: https://mlsanity.com/paper/2606.26168/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 6 of 6)

- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Autonomous Learning of Generative Models with Chemical Reaction Network Ensembles](https://mlsanity.com/paper/2311.00975/) (2023)
- [Programming Discrete Distributions with Chemical Reaction Networks](https://mlsanity.com/paper/1601.02578/) (2016)
- [A reaction network scheme which implements inference and learning for Hidden Markov Models](https://mlsanity.com/paper/1906.09410/) (2019)
- [Task-Guided Inverse Reinforcement Learning Under Partial Information](https://mlsanity.com/paper/2105.14073/) (2021)
- [Run-and-tumble chemotaxis using reinforcement learning](https://mlsanity.com/paper/2501.03687/) (2025)

## Built on by (most cited first, 1 of 1)

- [Reduction of Probabilistic Chemical Reaction Networks](https://mlsanity.com/paper/2606.27737/) (2026)

## Abstract (arXiv)

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 biochemical reaction dynamics. Environmental variables are hidden, while the cell updates a minimal internal state from each observation through a memoryless Bayesian step. These internal dynamics balance orienting toward light with exploratory reorientation and can be implemented through Chemical-Reaction-Network Ordinary Differential Equations (CRN--ODEs). Our model includes a biophysical observation process for photoreception and a chemically computable polynomial bound on information gain. Using Inverse Reinforcement Learning (IRL) on 30 experimentally recorded Chlamydomonas trajectories, we infer the behavioral objective consistent with observed phototactic motion and benchmark the resulting dynamics with standard Stochastic Simulation Algorithm (SSA) baselines. Our model reproduces the empirical alignment-to-light distribution, comparable to objective SSA baselines on this dataset. Within this framework, run--tumble alternation emerges as an information-acquisition strategy: tumbling reorients the cell to sample new sensory configurations and resolve sensor ambiguity, demonstrating how intracellular biochemical networks can support adaptive information-seeking behavior in cellular navigation.