Neoclassical analysis of the labor market and its institutions. A systematic development of the theory of labor supply …

Reinforcement Learning (RL) is a learning framework in which an agent learns a policy from continual interaction with … state construction. The project introduces a new method that applies Information Theory and decision tree technique to … have shown that our variant of the U-Tree (McCallum, 1995) produces a more robust state representation and faster learning …

made. Recently, stimulus-response (S-R) learning models have been proposed as explanations for decision makers' adaptation …

, based on inductive, competitive reinforcement-learning behaviour of atomistic heterogeneous agents in highly uncertain …

, based on inductive, competitive reinforcement-learning behaviour of atomistic heterogeneous agents in highly uncertain …

application of a reinforcement learning algorithm. The third essay presents an empirical analysis of new product launches in the …

While a number of algorithms for multiobjective reinforcement learning have been proposed, and a small number of … studies. Two classes of multiobjective reinforcement learning algorithms are identified, and appropriate evaluation metrics … are demonstrated via an empirical comparison of two example learning algorithms. ?? 2010 The Author(s). …

variant of prospect theory assuming that the sensitivity to the weighted values decreases with the distance between the …