A novel approach for multi-agent cooperative pursuit to capture grouped evaders

Qadir, Muhammad Zuhair; Piao, Songhao; Jiang, Haiyang; Souidi, Mohammed El Habib

doi:10.1007/s11227-018-2591-3

Cited by 12 publications

(6 citation statements)

References 21 publications

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“…Qadir et al [31] affirmed, that in AGR model, there is no mechanism defining the access conditions regarding the groups. Consequently, in AGRMF model, they used a binary variable instead of logic fuzzy set called degree of membership.…”

Section: Related Workmentioning

confidence: 99%

New Game-Theoretic Convolutional Neural Network Applied for the Multi-Pursuer Multi-Evader Game

Sid,

Djezzar,

Souidi

et al. 2023

cai

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Pursuit-Evasion Game (PEG) can be defined as a set of agents known as pursuers, which cooperate with the aim forming dynamic coalitions to capture dynamic evader agents, while the evaders try to avoid this capture by moving in the environment according to specific velocities. The factor of capturing time was treated by various studies before, but remain the powerful tools used to satisfy this factor object of research. To improve the capturing time factor we proposed in this work a novel online decentralized coalition formation algorithm equipped with Convolutional Neural Network (CNN) and based on the Iterated Elimination of Dominated Strategies (IEDS). The coalition is formed such that the pursuer should learn at each iteration the approximator formation achieving the capture in the shortest time. The pursuer's learning process depends on the features extracted by CNN at each iteration. The proposed supervised technique is compared through simulation, with the IEDS algorithm, AGR algorithm. Simulation results show that the proposed learning technique outperform the IEDS algorithm and the AGR algorithm with respect to the learning time which represents an important factor in a chasing game.

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Section: Related Workmentioning

confidence: 99%

New Game-Theoretic Convolutional Neural Network Applied for the Multi-Pursuer Multi-Evader Game

Sid,

Djezzar,

Souidi

et al. 2023

cai

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show abstract

“…While many recent studies on multi-player pursuit-evasion consider a single evading target whether in the framework of MARL [9], [26]- [29] or distributed control [30]- [32], our efforts to address the pursuit of multiple superior evaders, specially in MARL framework, highlights the value of this study. In [33], tackling multiple evaders is attempted in a multiagent pursuit based on application of self-organizing feature map and reinforcement learning. However, our work enables heterogeneous explorative agents alongside multiple-targettracking agents in decentralized MARL framework which has better maneuvering capability compared to [33].…”

Section: Related Workmentioning

confidence: 99%

“…In [33], tackling multiple evaders is attempted in a multiagent pursuit based on application of self-organizing feature map and reinforcement learning. However, our work enables heterogeneous explorative agents alongside multiple-targettracking agents in decentralized MARL framework which has better maneuvering capability compared to [33].…”

Section: Related Workmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Dynamic Ocean Monitoring by a Swarm of Buoys

Kouzehgar

Meghjani

Bouffanais

2020

Global Oceans 2020: Singapore – U.S. Gulf Coast

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Multi-agent pursuit-evasion tasks involving intelligent targets are notoriously challenging coordination problems. In this paper, we investigate new ways to learn such coordinated behaviors of unmanned aerial vehicles (UAVs) aimed at keeping track of multiple evasive targets. Within a Multi-Agent Reinforcement Learning (MARL) framework, we specifically propose a variant of the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method. Our approach addresses multitarget pursuit-evasion scenarios within non-stationary and unknown environments with random obstacles. In addition, given the critical role played by collective exploration in terms of detecting possible targets, we implement heterogeneous roles for the pursuers for enhanced exploratory actions balanced by exploitation (i.e. tracking) of previously identified targets. Our proposed role-based MADDPG algorithm is not only able to track multiple targets, but also is able to explore for possible targets by means of the proposed Voronoi-based rewarding policy. We implemented, tested and validated our approach in a simulation environment prior to deploying a real-world multi-robot system comprising of Crazyflie drones. Our results demonstrate that a multi-agent pursuit team has the ability to learn highly efficient coordinated control policies in terms of target tracking and exploration even when confronted with multiple fast evasive targets in complex environments.

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“…To allow the coalition of the pursuers with similar features, the extracted features are processed via a self-organizing map layer. On the other hand, in [12], the authors used K-means [13] in order to group the similar evaders characterized by the best parameters among the data set.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Dynamic Leader-Follower Path Planning Applied to the Multi-Pursuer Multi-Evader Game

Souidi,

Ledmi,

Maarouk

et al. 2023

cai

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Multi-agent collaborative path planning focuses on how the agents have to coordinate their displacements in the environment to achieve different targets or to cover a specific zone in a minimum of time. Reinforcement learning is often used to control the agents' trajectories in the case of static or dynamic targets. In this paper, we propose a multi-agent collaborative path planning based on reinforcement learning and leader-follower principles. The main objectives of this work are the development of an applicable motion planning in a partially observable environment, and also, to improve the agents' cooperation level during the tasks' execution via the creation of a dynamic hierarchy in the pursuit groups. This dynamic hierarchy is reflected by the possibility of reattributing the roles of Leaders and Followers at each iteration in the case of mobile agents to decrease the task's execution time. The proposed approach is applied to the Multi-Pursuer Multi-Evader game in comparison with recently proposed path planning algorithms dealing with the same problem. The simulation results reflect how this approach improves the pursuit capturing time and the payoff acquisition during the pursuit.

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A novel approach for multi-agent cooperative pursuit to capture grouped evaders

Cited by 12 publications

References 21 publications

New Game-Theoretic Convolutional Neural Network Applied for the Multi-Pursuer Multi-Evader Game

New Game-Theoretic Convolutional Neural Network Applied for the Multi-Pursuer Multi-Evader Game

Multi-Agent Reinforcement Learning for Dynamic Ocean Monitoring by a Swarm of Buoys

Multi-Agent Dynamic Leader-Follower Path Planning Applied to the Multi-Pursuer Multi-Evader Game

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