We present a learning framework for Markovian decision processes that is based on optimization in the policy space. Instead of using relatively slow gradient-based optimization al...
While synaptic learning mechanisms have always been a core topic of neural computation research, there has been relatively little work on intrinsic learning processes, which change...
Data mining techniques and machine learning methods are commonly used in several disciplines. It is possible that they could also provide a basis for quality assessment of softwar...
We report new results on the corner classification approach to training feedforward neural networks. It is shown that a prescriptive learning procedure where the weights are simp...
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 ...