Solving in an efficient manner many different optimal control tasks within the same underlying environment requires decomposing the environment into its computationally elemental ...
Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms co...
Pascal Poupart, Nikos A. Vlassis, Jesse Hoey, Kevi...
Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications in learning from demonstration or apprenticeship l...
Abstract. The implementation of high-performance robot controllers for complex control tasks such as playing autonomous robot soccer is tedious, errorprone, and a never ending prog...
— Many neural network models of (human) motor learning focus on the acquisition of direct goal-to-action mappings, which results in rather inflexible motor control programs. We ...