In this paper we consider the problem of policy evaluation in reinforcement learning, i.e., learning the value function of a fixed policy, using the least-squares temporal-differe...
Alessandro Lazaric, Mohammad Ghavamzadeh, Ré...
: This article gives an overview of the Essex Wizards 2000 team participated in the RoboCup 2000 simulator league. A brief description of the agent architecture for the team is int...
Huosheng Hu, Kostas Kostiadis, Matthew Hunter, Kos...
Natural policy gradient methods and the covariance matrix adaptation evolution strategy, two variable metric methods proposed for solving reinforcement learning tasks, are contrast...
Recently, we have introduced a novel approach to dynamic programming and reinforcement learning that is based on maintaining explicit representations of stationary distributions i...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...
In order to claim fully general intelligence in an autonomous agent, the ability to learn is one of the most central capabilities. Classical machine learning techniques have had ma...