We model reinforcement learning as the problem of learning to control a Partially Observable Markov Decision Process ( ¢¡¤£¦¥§ ), and focus on gradient ascent approache...
In this paper we present the system ALLPAD for learning Logic Programs with Annotated Disjunctions (LPADs). ALLPAD modifies the previous system LLPAD in order to tackle real world ...
We describe a neurally-inspired, unsupervised learning algorithm that builds a non-linear generative model for pairs of face images from the same individual. Individuals are then ...
— In this paper, we propose a reinforcement learning approach to address multi-robot cooperative navigation tasks in infinite settings. We propose an algorithm to simultaneously...
Reducing management costs and improving the availability of large-scale distributed systems require automatic replica regeneration, i.e., creating new replicas in response to repl...