Optimal resource scheduling in multiagent systems is a computationally challenging task, particularly when the values of resources are not additive. We consider the combinatorial ...
Dmitri A. Dolgov, Michael R. James, Michael E. Sam...
We present Policy Gradient Actor-Critic (PGAC), a new model-free Reinforcement Learning (RL) method for creating limited-memory stochastic policies for Partially Observable Markov ...
In this paper, we utilize a predator-prey model in order to identify characteristics of single-objective variation operators in the multi-objective problem domain. In detail, we a...
Christian Grimme, Joachim Lepping, Alexander Papas...
We consider the problem of verifying reachability properties of stochastic real-time systems modeled as generalized semi-Markov processes (GSMPs). The standard simulation-based tec...
Abstract. Two main challenges of robot action planning in real domains are uncertain action effects and dynamic environments. In this paper, an instance-based action model is lear...