In reinforcement learning, least-squares temporal difference methods (e.g., LSTD and LSPI) are effective, data-efficient techniques for policy evaluation and control with linear v...
Michael H. Bowling, Alborz Geramifard, David Winga...
Temporal difference (TD) algorithms are attractive for reinforcement learning due to their ease-of-implementation and use of "bootstrapped" return estimates to make effi...
Our goal is to provide learning mechanisms to game agents so they are capable of adapting to new behaviors based on the actions of other agents. We introduce a new on-line reinfor...
Mediation is the process of decomposing a task into subtasks, finding agents suitable for these subtasks and negotiating with agents to obtain commitments to execute these subtas...
The problem of recognizing actions in realistic videos
is challenging yet absorbing owing to its great potentials
in many practical applications. Most previous research is
limit...
Jintao Li, Ju Sun, Loong Fah Cheong, Shuicheng Yan...