APPLYING REINFORCEMENT LEARNING TO THE WEAPON ASSIGNMENT PROBLEM IN AIR DEFENCE

The Weapon-Target Assignment Problem

Efficient Heuristic Approach to the Weapon-Target Assignment Problem

The Weapon Target Assignment (WTA) problem is a fundamental defense application of operations research. The problem consists of optimally assigning a given number of weapons to a set of targets so that the post-engagement total expected survival value of the targets is minimized. Since the WTA problem is known to be NP-complete, there are no exact methods to solve it. This paper presents two in...

Applying reinforcement learning to Tetris

This paper investigates the possible application of reinforcement learning to Tetris. The author investigates the background of Tetris, and qualifies it in a mathematical context. The author discusses reinforcement learning, and considers historically successful applications of it. Finally the author discusses considerations surrounding implementation.

A Multiagent Reinforcement Learning algorithm to solve the Community Detection Problem

Community detection is a challenging optimization problem that consists of searching for communities that belong to a network under the assumption that the nodes of the same community share properties that enable the detection of new characteristics or functional relationships in the network. Although there are many algorithms developed for community detection, most of them are unsuitable when ...

Exact and Heuristic Methods for the Weapon Target Assignment Problem

The Weapon Target Assignment (WTA) problem is a fundamental problem arising in defense-related applications of operations research. This problem consists of optimally assigning n weapons to m targets so that the total expected survival value of the targets after all the engagements is minimum. The WTA problem can be formulated as a nonlinear integer programming problem and is known to be NP-com...