Abstract. This paper explores the capabilities of continuous time recurrent neural networks (CTRNNs) to display reinforcement learning-like abilities on a set of T-Maze and double ...
Abstract—Today we can identify a big gap between requirement specification and the generation of test environments. This article extends the Classification Tree Method for Embe...
Abstract: This paper considers the task of trajectory stabilization for a fish-like robot by means of feedback. We use oscillatory control inputs and apply correction signals at t...
Kristi A. Morgansen, Patricio A. Vela, Joel W. Bur...
Abstract— We investigate an optimal consumption and investment problem where we receive a certain fixed income stream that is terminated at a random time. It turns out that the ...
Abstract— In this paper we study bipartite, first ordernetworks where the nodes take on leader or follower roles. In particular, we let the leaders’ positions be static and as...
Giuseppe Notarstefano, Magnus Egerstedt, Musad Haq...