A Decentralized Fuzzy Learning Algorithm for Pursuit-Evasion Differential Games with Superior Evaders

Awheda, Mostafa D.; Schwartz, Howard M.

doi:10.1007/s10846-015-0315-y

Cited by 51 publications

(31 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We evaluate the proposed algorithm over a number of multi-pursuer single-superior-evader pursuit-evasion differential games, and the results validate the proposed algorithm. The proposed algorithm is published in [139].…”

Section: Summary and Discussionmentioning

confidence: 99%

On Multi-Agent Reinforcement Learning in Matrix, Stochastic and Differential Games

Awheda¹

View full text Add to dashboard Cite

In this thesis, we investigate reinforcement learning algorithms on matrix, stochastic, and differential games. In matrix and stochastic games, the states and actions are represented in continuous domains. We propose two decentralized multi-agent reinforcement learning algorithms to solve the problem of learning in matrix and stochastic games when the learning agent has only minimum knowledge about the underlying game and the other learning agents. The proposed algorithms are the constant learning rate-based exponential moving average Q-learning (CLR-EMAQL) algorithm, and the exponential moving average Q-learning (EMAQL) algorithm. We mathematically show that the proposed CLR-EMAQL algorithm converges to Nash equilibrium in games with pure Nash equilibrium. We introduce the concept of Win-or-Learn-Slow (WoLS) mechanism for the proposed EMAQL algorithm so that the proposed algorithm learns fast when it is winning, and learns cautiously when it is losing. We also provide a theoretical proof of convergence to Nash equilibrium for the proposed EMAQL algorithm in games with pure Nash equilibrium. In games with mixed Nash equilibrium, our mathematical analysis shows that the proposed EMAQL algorithm converges to an equilibrium. Although our mathematical analysis does not explicitly show that the proposed EMAQL algorithm converges to Nash equilibrium, our simulation results indicate that the proposed EMAQL algorithm does converge to Nash equilibrium. Our simulation iii I would like to express my deepest gratitude to my advisor, Professor Howard M.

show abstract

Section: Summary and Discussionmentioning

confidence: 99%

On Multi-Agent Reinforcement Learning in Matrix, Stochastic and Differential Games

Awheda¹

View full text Add to dashboard Cite

show abstract

“…As mentioned earlier, although there are numerous papers discussing different methods, including learning methods to solve the problem of the PE game with slow evaders, there are only a few that address multi-player PE differential games with superior evaders [8,[23][24][25][132][133][134][135][136]. The authors in [8] proposed a sufficiency condition to capture superior evaders.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This learning algorithm is based on the condition that was proposed in [8], as well as a specific formation control strategy. The second learning algorithm [136] is based on fuzzy-reinforcement learning with Apollonius circles and a modified formation control strategy. The goal of a superior evader is to learn how to reach a specific target, and the goal of the pursuers is to learn how to cooperate to capture the superior evader.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, when the distance between the evader and the nearest pursuer is less than a specific tolerance distance, the strategy of the evader is to run away from that pursuer. In [23,136], there is a necessity to calculate the capture angle to obtain the required control signal for each pursuer to capture the superior evader. This thesis proposes a new reward function formulation for the FACLA algorithm, that enables a group of pursuers to capture a single evader in a decentralized manner and without finding the capture angle.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, Awheda et. al [23,136] recently proposed two decentralized learning algorithms for the PE game issue with a single superior-evader. The first learning algorithm [23] was used to enable a group of pursuers with equal speed to capture a single evader that has a speed identical to the speed of the pursuers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning in the Multi-Robot Pursuit Evasion Game

Al-Talabi¹

View full text Add to dashboard Cite

This thesis proposes different learning algorithms to investigate the learning issues of mobile robots playing differential forms of the Pursuit-Evasion (PE) game. The algorithms are used to reduce: (1) the computational requirements without affecting the overall performance of the algorithm, (2) the learning time, (3) the capture time and the possibility of collision among pursuers and (4) to deal multi-robot PE First and foremost, I am extremely grateful to Almighty Allah, and to Ahlul-Bayt, peace be upon them, for their countless blessings and for the great care they have provided at every moment of my life. Certainly, without their support and bounty, I would never successfully finish this chapter of my life by getting the PhD degree.

show abstract

Research on Methods of Region Searching and Cooperative Hunting of UAVs for Maritime Target

Xie

Zhang

Zeng

et al. 2022

Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021)

View full text Add to dashboard Cite

A Decentralized Fuzzy Learning Algorithm for Pursuit-Evasion Differential Games with Superior Evaders

Cited by 51 publications

References 33 publications

On Multi-Agent Reinforcement Learning in Matrix, Stochastic and Differential Games

On Multi-Agent Reinforcement Learning in Matrix, Stochastic and Differential Games

Learning in the Multi-Robot Pursuit Evasion Game

Research on Methods of Region Searching and Cooperative Hunting of UAVs for Maritime Target

Contact Info

Product

Resources

About