Evaluating cooperative-competitive dynamics with deep Q-learning

Kopacz, Anikó; Csató, Lehel; Chira, Camelia

doi:10.1016/j.neucom.2023.126507

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Kopacz et al modeled cooperative-competitive social group dynamics with multi-agent environments and used three methods to solve the multi-agent optimization problem. It was found that training in a decentralized manner with the DQN outperforms both the monotonic value factorization methods (QMIX) and the multiagent variational exploration approach (MAVEN) on the analyzed cooperative-competitive environments for both agent types [20]. Mukhtar et al developed the Deep Graph Convolution Q-Network (DGCQ) by combining the DQN and the Graph Convolutional Network (GCN) to achieve a signal-free corridor.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Routing Policies for Multi-Skill Call Centers Using Deep Q Network

Zhang

2023

Mathematics

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When the call center queuing system becomes complex, it turns out that the static routing policy is not optimal. This paper considers the problem of the dynamic routing policy for call centers with multiple skill types and agent groups. A state-dependent routing policy based on the Deep Q Network (DQN) is proposed, and a reinforcement learning algorithm is applied to optimize the routing. A simulation algorithm is designed to help customers and agents interact with the external environment to learn the optimal strategy. The performance evaluation considered in this paper is the service level/abandon rate. Experiments show that the DQN-based dynamic routing policy performs better than the common static policy Global First Come First Serve (FCFS) and the dynamic policy Priorities with Idle Agent Thresholds and Weight-Based Routing in various examples. On the other hand, the training time of the routing policy model based on the DQN is much faster than routing optimization based on simulation and a genetic algorithm.

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Section: Introductionmentioning

confidence: 99%

Dynamic Routing Policies for Multi-Skill Call Centers Using Deep Q Network

Zhang

2023

Mathematics

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Pursuit Path Planning for Multiple Unmanned Ground Vehicles Based on Deep Reinforcement Learning

Guo,

Xu,

et al. 2023

Electronics

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Path planning plays a crucial role in the execution of pursuit tasks for multiple unmanned ground vehicles (multi-UGVs). Although existing popular path-planning methods can achieve the pursuit goals, they suffer from some drawbacks such as long computation time and excessive path inflection points. To address these issues, this paper combines gradient descent and deep reinforcement learning (DRL) to solve the problem of excessive path inflection points from a path-smoothing perspective. In addition, the prioritized experience replay (PER) method is incorporated to enhance the learning efficiency of DRL. By doing so, the proposed model integrates PER, gradient descent, and a multiple-agent double deep Q-learning network (PER-GDMADDQN) to enable the path planning and obstacle avoidance capabilities of multi-UGVs. Experimental results demonstrate that the proposed PER-GDMADDQN yields superior performance in the pursuit problem of multi-UGVs, where the training speed and smoothness of the proposed method outperform other popular algorithms. As a result, the proposed method enables satisfactory path planning for multi-UGVs.

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Evaluating cooperative-competitive dynamics with deep Q-learning

Cited by 2 publications

References 11 publications

Dynamic Routing Policies for Multi-Skill Call Centers Using Deep Q Network

Dynamic Routing Policies for Multi-Skill Call Centers Using Deep Q Network

Pursuit Path Planning for Multiple Unmanned Ground Vehicles Based on Deep Reinforcement Learning

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