Multi-agent Pursuit-Evasion Under Uncertainties with Redundant Robot Assignments: Extended Abstract

2019 ◽  
Author(s):  
Leiming Zhang ◽  
Amanda Prorok ◽  
Subhrajit Bhattacharya
Keyword(s):  
Redundant Robot ◽  
Pursuit Evasion ◽  
Multi Agent

scholarly journals A Stochastic Characterization of the Capture Zone in Pursuit-Evasion Games

Games ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 54
Author(s):  
Simone Battistini
Keyword(s):  
Optimal Strategies ◽  
Capture Zone ◽  
Pursuit Evasion ◽  
Guidance Strategies ◽  
Stochastic Characterization ◽  
Evasion Game ◽  
Multi Agent ◽  
Planning Problems ◽  
Insight Into

Pursuit-evasion games are used to define guidance strategies for multi-agent planning problems. Although optimal strategies exist for deterministic scenarios, in the case when information about the opponent players is imperfect, it is important to evaluate the effect of uncertainties on the estimated variables. This paper proposes a method to characterize the game space of a pursuit-evasion game under a stochastic perspective. The Mahalanobis distance is used as a metric to determine the levels of confidence in the estimation of the Zero Effort Miss across the capture zone. This information can be used to gain an insight into the guidance strategy. A simulation is carried out to provide numerical results.

scholarly journals An Improved Approach towards Multi-Agent Pursuit–Evasion Game Decision-Making Using Deep Reinforcement Learning

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1433
Author(s):  
Kaifang Wan ◽  
Dingwei Wu ◽  
Yiwei Zhai ◽  
Bo Li ◽  
Xiaoguang Gao ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Superior Performance ◽  
State Variables ◽  
Multi Agent Systems ◽  
Adversarial Learning ◽  
Pursuit Evasion ◽  
Evasion Game ◽  
Multi Agent ◽  
Adversarial Attack

A pursuit–evasion game is a classical maneuver confrontation problem in the multi-agent systems (MASs) domain. An online decision technique based on deep reinforcement learning (DRL) was developed in this paper to address the problem of environment sensing and decision-making in pursuit–evasion games. A control-oriented framework developed from the DRL-based multi-agent deep deterministic policy gradient (MADDPG) algorithm was built to implement multi-agent cooperative decision-making to overcome the limitation of the tedious state variables required for the traditionally complicated modeling process. To address the effects of errors between a model and a real scenario, this paper introduces adversarial disturbances. It also proposes a novel adversarial attack trick and adversarial learning MADDPG (A2-MADDPG) algorithm. By introducing an adversarial attack trick for the agents themselves, uncertainties of the real world are modeled, thereby optimizing robust training. During the training process, adversarial learning was incorporated into our algorithm to preprocess the actions of multiple agents, which enabled them to properly respond to uncertain dynamic changes in MASs. Experimental results verified that the proposed approach provides superior performance and effectiveness for pursuers and evaders, and both can learn the corresponding confrontational strategy during training.

An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game

2011 ◽  
pp. 77-98
Author(s):  
Malgorzata Lucinska ◽  
Slawomir T. Wierzchon
Keyword(s):  
Learning Strategies ◽  
Autonomous Agents ◽  
Clonal Selection ◽  
Hybrid Approach ◽  
Point Of View ◽  
Distributed Programming ◽  
Multi Agent Systems ◽  
Pursuit Evasion ◽  
Multi Agent ◽  
Complex Relationships

Multi-agent systems (MAS), consist of a number of autonomous agents, which interact with one-another. To make such interactions successful, they will require the ability to cooperate, coordinate, and negotiate with each other. From a theoretical point of view such systems require a hybrid approach involving game theory, artificial intelligence, and distributed programming. On the other hand, biology offers a number of inspirations showing how these interactions are effectively realized in real world situations. Swarm organizations, like ant colonies or bird flocks, provide a spectrum of metaphors offering interesting models of collective problem solving. Immune system, involving complex relationships among antigens and antibodies, is another example of a multi-agent and swarm system. In this chapter an application of so-called clonal selection algorithm, inspired by the real mechanism of immune response, is proposed to solve the problem of learning strategies in the pursuit-evasion problem.

scholarly journals A2CM: a new multi-agent algorithm

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 28
Author(s):  
Gabor Paczolay ◽  
Istvan Harmati
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Multiple Agents ◽  
Pursuit Evasion ◽  
The World ◽  
Multi Agent ◽  
New Algorithms ◽  
Modified Algorithm

<p class="Abstract">Reinforcement learning is currently one of the most researched fields of artificial intelligence. New algorithms are being developed that use neural networks to compute the selected action, especially for deep reinforcement learning. One subcategory of reinforcement learning is multi-agent reinforcement learning, in which multiple agents are present in the world. As it involves the simulation of an environment, it can be applied to robotics as well. In our paper, we use our modified version of the advantage actor–critic (A2C) algorithm, which is suitable for multi-agent scenarios. We test this modified algorithm on our testbed, a cooperative–competitive pursuit–evasion environment, and later we address the problem of collision avoidance.</p>

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