By using a macro/micro state model we show how assumptions on the resolution of logical and physical timing of computation in computer systems has resulted in design methodologies...
— The least squares approach works efficiently in value function approximation, given appropriate basis functions. Because of its smoothness, the Gaussian kernel is a popular an...
Masashi Sugiyama, Hirotaka Hachiya, Christopher To...
We address the problem of coordinating the plans and schedules for a team of agents in an uncertain and dynamic environment. Bounded rationality, bounded communication, subjectivi...
Inverse Reinforcement Learning (IRL) is the problem of learning the reward function underlying a Markov Decision Process given the dynamics of the system and the behaviour of an e...
We present a new algorithm, GM-Sarsa(0), for finding approximate solutions to multiple-goal reinforcement learning problems that are modeled as composite Markov decision processe...