Building upon the interactive inversion method introduced by Ashburn and Bonabeau (2004), we show how to dramatically improve the results by exploiting modularity and by letting t...
Abstract. We consider Reinforcement Learning for average reward zerosum stochastic games. We present and analyze two algorithms. The first is based on relative Q-learning and the ...
In order for a neural network ensemble to generalise properly, two factors are considered vital. One is the diversity and the other is the accuracy of the networks that comprise th...
In previous work, we proposed a unique landmark-based map learning method for mobile robots based on the “co-visibility” information i.e., very coarse qualitative information o...
Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an ...