We propose a neural network (NN) approach that yields approximate solutions for high-
dimensional optimal control (OC) problems and demonstrate its effectiveness using …

We propose a neural network approach for solving high-dimensional optimal control
problems. In particular, we focus on multi-agent control problems with obstacle and collision …

We consider the problem of designing distributed collision-avoidance multi-agent control in
large-scale environments with potentially moving obstacles, where a large number of agents …

This work presents a method for multi-robot coordination based on a novel distributed
nonlinear model predictive control (NMPC) formulation for trajectory optimization and its …

Deep reinforcement learning in continuous domains focuses on learning control policies that
map states to distributions over actions that ideally concentrate on the optimal choices in …

In this work, we develop a scalable, local tra-jectory optimization algorithm that enables
robots to interact with other robots. It has been shown that agents' interactions can be …

In this paper, the problem of learning policies to control a large number of homogeneous
robots is considered. To this end, we propose a new algorithm we call Graph Policy …

Developing a safe and efficient collision-avoidance policy for multiple robots is challenging
in the decentralized scenarios where each robot generates its paths with limited observation …

In this paper, we develop randomized block-coordinate descent methods for minimizing
multi-agent convex optimization problems with linearly coupled constraints over networks …

In this paper, we present a nonlinear model predictive control (NMPC) for multiple
autonomous helicopters in a complex environment. The NMPC provides a framework to …