Learning the sound inventory of a complex vocal skill via an intrinsic reward.
Sci Adv
; 10(13): eadj3824, 2024 Mar 29.
Article
em En
| MEDLINE
| ID: mdl-38536920
ABSTRACT
Reinforcement learning (RL) is thought to underlie the acquisition of vocal skills like birdsong and speech, where sounding like one's "tutor" is rewarding. However, what RL strategy generates the rich sound inventories for song or speech? We find that the standard actor-critic model of birdsong learning fails to explain juvenile zebra finches' efficient learning of multiple syllables. However, when we replace a single actor with multiple independent actors that jointly maximize a common intrinsic reward, then birds' empirical learning trajectories are accurately reproduced. The influence of each actor (syllable) on the magnitude of global reward is competitively determined by its acoustic similarity to target syllables. This leads to each actor matching the target it is closest to and, occasionally, to the competitive exclusion of an actor from the learning process (i.e., the learned song). We propose that a competitive-cooperative multi-actor RL (MARL) algorithm is key for the efficient learning of the action inventory of a complex skill.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Tentilhões
Limite:
Animals
Idioma:
En
Revista:
Sci Adv
/
Sci. Adv
/
Science advances
Ano de publicação:
2024
Tipo de documento:
Article
País de publicação:
Estados Unidos