Learning how to find targets in the micro-world: the case of intermittent active Brownian particles.
Soft Matter
; 20(9): 2008-2016, 2024 Feb 28.
Article
em En
| MEDLINE
| ID: mdl-38328899
ABSTRACT
Finding the best strategy to minimize the time needed to find a given target is a crucial task both in nature and in reaching decisive technological advances. By considering learning agents able to switch their dynamics between standard and active Brownian motion, here we focus on developing effective target-search behavioral policies for microswimmers navigating a homogeneous environment and searching for targets of unknown position. We exploit projective simulation, a reinforcement learning algorithm, to acquire an efficient stochastic policy represented by the probability of switching the phase, i.e. the navigation mode, in response to the type and the duration of the current phase. Our findings reveal that the target-search efficiency increases with the particle's self-propulsion during the active phase and that, while the optimal duration of the passive case decreases monotonically with the activity, the optimal duration of the active phase displays a non-monotonic behavior.
Texto completo:
1
Base de dados:
MEDLINE
Idioma:
En
Revista:
Soft Matter
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Áustria