Model learning combined with dynamic programming has been shown to be e ective for learning control of continuous state dynamic systems. The simplest method assumes the learned mod...
Training datasets for object detection problems are typically very large and Support Vector Machine (SVM) implementations are computationally complex. As opposed to these complex ...
Abstract. Infinite-horizon multi-agent control processes with nondeterminism and partial state knowledge have particularly interesting properties with respect to adaptive control, ...
This paper develops a variant of Simulated Annealing (SA) algorithm for solving discrete stochastic optimization problems where the objective function is stochastic and can be eva...
Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on ...