| Autor: |
Toutounji H; Department of Psychology, University of Sheffield, Sheffield S1 4DP, UK.; Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield S1 3JD, UK.; The Neuroscience Institute, University of Sheffield, Sheffield S10 2HQ, UK., Zai AT; Institute of Neuroinformatics, University of Zurich/ETH Zurich, Zurich CH-8057, Switzerland.; Neuroscience Center Zurich (ZNZ), Zurich CH-8057, Switzerland., Tchernichovski O; Department of Psychology, Hunter College, The City University of New York, New York, NY 10065, USA., Hahnloser RHR; Institute of Neuroinformatics, University of Zurich/ETH Zurich, Zurich CH-8057, Switzerland.; Neuroscience Center Zurich (ZNZ), Zurich CH-8057, Switzerland., Lipkind D; Department of Biology, York College, The City University of New York, New York, NY 11451, USA. |
| Abstrakt: |
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. |
