initial reward
R-learninginactor-criticmodeloffersabiologically relevantmechanismforsequentialdecision-making
Afewstudies haveexplored sequential stay-or-leavedecisions in humans, or rodents - the model organism used to access neuronal activity at high resolution. In both cases, decision patterns were collected inforaging tasks-the experimental settings where subjects decide when to leave depleting resources (2).
Country: North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.47)
Methods To study the ways of how real-world agents may learn to make sequential decisions, we built models
We used these rewards to update the network's outputs as follows.
Methods
We used these rewards to update the network's outputs as follows.
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.46)
R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making
In everyday life we repeatedly face sequential stay-or-leave decisions.
Country:
- North America > United States (0.14)
- North America > Canada (0.04)