A Strategy-Based Algorithm for Moving Targets in an Environment with Multiple Agents

Abstract

Most studies in the field of search algorithms have only focused on pursuing agents, while comparatively less attention has been paid to target algorithms that employ strategies to evade multiple pursuing agents. In this study, a state-of-the-art target algorithm, TrailMax, has been enhanced and implemented for multiple agent pathfinding problems. The presented algorithm aims to maximise the capture time if possible until timeout. Empirical analysis is performed on grid-based gaming benchmarks, measuring the capture cost, the success of escape and statistically analysing the results. The new algorithm, Multiple Pursuers TrailMax, doubles the escaping time steps until capture when compared with existing target algorithms and increases the target’s escaping success by 13% and in some individual cases by 37%.

Consistent Epistemic Planning for Multiagent Deep Reinforcement Learning

“…Target search and tracking (S&T) is a sustained and focused research topic in recent years [1], [2], [5], [6], [8]- [11], which refers to detecting an object of interest in a search space [1] and tracking it. The related research can be divided into two components -1) to design a filter using a series of past observations to get correct estimates of the opponent location [3], [4], [6], [12] and 2) to improve policies for the S&T agents by leveraging the estimation from the filtering module [1], [2], [5], [8]- [10], [13] The classic Kalman-based filters have been widely used in this field.…”

“…We assume the action a i to be the searching agent velocity. Samples are randomly drawn from the replay buffer D to train the actor-critic and it should be noticed that all the gradients back-propagation is cut off at observation, which means we only update the actor-critic network parameters using equation (11) and (12). PMC is updated only with ground truth location and negative log likelihood.…”