We empirically evaluate the performance of various reinforcement learning methods in applications to sequential targeted marketing. In particular, we propose and evaluate a progre...
Naoki Abe, Edwin P. D. Pednault, Haixun Wang, Bian...
We consider the setting of multiple collaborative agents trying to complete a set of tasks as assigned by a centralized controller. We propose a scalable method called“Assignmen...
The options framework provides a method for reinforcement learning agents to build new high-level skills. However, since options are usually learned in the same state space as the...
Bayesian Reinforcement Learning has generated substantial interest recently, as it provides an elegant solution to the exploration-exploitation trade-off in reinforcement learning...
Abstract. Choosing between multiple alternative tasks is a hard problem for agents evolving in an uncertain real-time multiagent environment. An example of such environment is the ...