The goal of Reinforcement learning (RL) is to maximize reward (minimize cost) in a Markov decision process (MDP) without knowing the underlying model a priori. RL algorithms tend ...
In this paper we propose a model for human learning and decision making in environments of repeated Cliff-Edge (CE) interactions. In CE environments, which include common daily in...
Temporal difference (TD) algorithms are attractive for reinforcement learning due to their ease-of-implementation and use of "bootstrapped" return estimates to make effi...
Abstract. This paper develops a model for tightness time, linkage learning time for a single building block, in the linkage learning genetic algorithm (LLGA). First, the existing m...
This paper explores the potential impact of collaborative technologies on improving management education. The first goal is to expose students to tools and practices that not only...