Michael L. Littman

Markov games as a framework for multi-agent reinforcement learning

Reinforcement Learning: A Survey

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both th...

Acting Optimally in Partially Observable Stochastic Domains

In this paper, we describe the partially observable Markov decision process (pomdp) approach to finding optimal or near-optimal control strategies for partially observable stoch...

Generalization and Scaling in Reinforcement Learning

In associative reinforcement learning, an environment generates input vectors, a learning system generates possible output vectors, and a reinforcement function computes feedbac...

Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus

The evaluative character of a word is called its semantic orientation. A positive semantic orientation implies desirability (e.g., "honest", "intrepid") and a negative semantic ...