Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …

A fundamental task in robotics is to plan collision‐free motions among a set of obstacles.
Recently, learning‐based motion‐planning methods have shown significant advantages in …

Chip design with machine learning (ML) has been widely explored to achieve better
designs, lower runtime costs, and no human-in-the-loop process. However, with tons of …

Motion prediction is essential and challenging for autonomous vehicles and social robots.
One challenge of motion prediction is to model the interaction among traffic actors, which …

In this paper, we introduce the Action Schema Network (ASNet): a neural network
architecture for learning generalised policies for probabilistic planning problems. By …

In this paper, we discuss the learning of generalised policies for probabilistic and classical
planning problems using Action Schema Networks (ASNets). The ASNet is a neural network …

In this paper we tackle the problem of routing multiple agents in a coordinated manner. This
is a complex problem that has a wide range of applications in fleet management to achieve …

Deep reinforcement learning has shown remarkable success in the past few years. Highly
complex sequential decision making problems have been solved in tasks such as game …

UAVs (Unmanned Aerial Vehicles) have been developed and adopted for various fields
including military, IT, agriculture, construction, and so on. In particular, UAVs are being …

Real-time path planning is crucial for intelligent vehicles to achieve autonomous navigation.
In this paper, we propose a novel deep neural network (DNN) based method for real-time …