Humans can learn under a wide variety of feedback conditions. Particularly important types of learning fall under the category of reinforcement learning (RL) where a series of decisions must be made and a sparse feedback signal is obtained. Computational and behavioral studies of RL have focused mainly on Markovian decision processes (MDPs), where the next state and reward depends only on the c...

Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where a series of rewarded decisions must be made, is a particularly important type of learning. Computational and behavioral studies of RL have focused mainly on Markovian decision processes, where the next state depends on only the current state and action. Little is known about non-Markovian decision m...

In this paper a two-state Markovian maintenance process where the true state is unknown will be considered. The operating cost per period is a continuous random variable which depends on the state of the process. If investigation cost is incurred at the beginning of any period, the system wit I be returned to the "in-control" state instantaneously. This problem is solved using the average crite...

This paper presents a state estimation approach for reinforcement learning (RL) of a partially observable Markov decision process. It is based on a special recurrent neural network architecture, the Markov decision process extraction network with shortcuts (MPEN-S). In contrast to previous work regarding this topic, we address the problem of long-term dependencies, which cause major problems in...

Several Markovian process calculi have been proposed in the literature, which differ from each other for various aspects. With regard to the action representation, we distinguish between integrated-time Markovian process calculi, in which every action has an exponentially distributed duration associated with it, and orthogonal-time Markovian process calculi, in which action execution is separat...