When controlling dynamic systems, such as mobile robots in uncertain environments, there is a trade off between risk and reward. For example, a race car can turn a corner faster b...
This paper describes some of the interactions of model learning algorithms and planning algorithms we have found in exploring model-based reinforcement learning. The paper focuses...
We consider the problem of cooperative multiagent planning under uncertainty, formalized as a decentralized partially observable Markov decision process (Dec-POMDP). Unfortunately...
Matthijs T. J. Spaan, Frans A. Oliehoek, Nikos A. ...
This paper proposes a comprehensive model for test planning in a core-based environment. The main contribution of this work is the use of several types of TAMs and the considerati...
— By combining a low-order model of forecast errors, the extended Kalman filter, and classical continuous optimization, we develop an integrated methodology for planning mobile ...