Abstract— Least-squares policy iteration is a useful reinforcement learning method in robotics due to its computational efficiency. However, it tends to be sensitive to outliers...
We propose a generic method for obtaining quickly good upper bounds on the minimal value of a multistage stochastic program. The method is based on the simulation of a feasible dec...
This work presents a generalized theoretical framework that allows incorporation of opponent models into adversary search. We present the M algorithm, a generalization of minimax ...
—Functional simulation is still the primary workhorse for verifying the functional correctness of hardware designs. Functional verification is necessarily incomplete because it i...
Trace-driven simulation of superscalar processors is particularly complicated. The dynamic nature of superscalar processors combined with the static nature of traces can lead to l...