Jan 4, 2020 · Here we develop a hierarchical model of supervisory control driven by reinforcement learning (RL). The supervisory level learns to switch using task-specific ...
Nov 5, 2020 · In task interleaving, we use it to model how people estimate the value of continuing in a task and can anticipate a high future reward even if ...
Jan 4, 2020 · A hierarchically op- timal value function decomposition can be learned from ex- perience, even in conditions with multiple tasks and arbitrary.
A hierarchical model of supervisory control driven by reinforcement learning (RL) is developed that reproduces known empirical effects of task interleaving ...
Hierarchical Reinforcement Learning as a Model of Human Task Interleaving. Christoph Gebhardt, Antti Oulasvirta, Otmar Hilliges. Published at. arXiv:2001.02122 ...
The model reproduces known empirical effects of task interleaving. It yields better predictions of individual-level data than a myopic baseline in a six-task ...
The model reproduces known empirical effects of task interleaving. It yields better predictions of individual-level data than a myopic baseline in a six-task ...
People also ask
What is hierarchical reinforcement learning?
What is an example of reinforcement learning in humans?
What is the main objective of reinforcement learning using human feedback?
What is inverse reinforcement learning learning from humans?
The model also reproduces well-known key phenomena of task interleaving, such as the sensitivity to costs of resumption and immediate as well as delayed in-task ...
The model also reproduces well-known key phenomena of task interleaving, such as the sensitivity to costs of resumption and immediate as well as delayed in-task ...
The model also reproduces well-known key phenomena of task interleaving, such as the sensitivity to costs of resumption and immediate as well as delayed in-task ...