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Found 6 papers, 4 papers with code
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs
Inverse optimal control can be used to characterize behavior in sequential decision-making tasks.
Reinforcement Learning with Non-Exponential Discounting
In contrast, in economics and psychology, it has been shown that humans often adopt a hyperbolic discounting scheme, which is optimal when a specific task termination time distribution is assumed.
Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System
Computational level explanations based on optimal feedback control with signal-dependent noise have been able to account for a vast array of phenomena in human sensorimotor behavior.
POMDPs in Continuous Time and Discrete Spaces
We consider the problem of optimal decision making in such discrete state and action space systems under partial observability.
Receding Horizon Curiosity
Sample-efficient exploration is crucial not only for discovering rewarding experiences but also for adapting to environment changes in a task-agnostic fashion.
Probabilistic Trajectory Segmentation by Means of Hierarchical Dirichlet Process Switching Linear Dynamical Systems
Using movement primitive libraries is an effective means to enable robots to solve more complex tasks.
