Some of the most successful recent applications of reinforcement learning have used neural networks and the TD algorithm to learn evaluation functions. In this paper, we examine t...
We introduce a Bayesian model, BayesANIL, that is capable of estimating uncertainties associated with the labeling process. Given a labeled or partially labeled training corpus of...
Production scheduling, the problem of sequentially con guring a factory to meet forecasted demands, is a critical problem throughout the manufacturing industry. The requirement of...
Jeff G. Schneider, Justin A. Boyan, Andrew W. Moor...
—Large-scale agent-based systems are required to self-optimize towards multiple, potentially conflicting, policies of varying spatial and temporal scope. As a result, not all ag...
Abstract. Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unkown value function can be obtained, is one of th...