Collaborative Q-learning path planning for autonomous robots based on holonic multi-agent system

Lamini, Chaymaa; Fathi, Youssef; Benhlima, Said

doi:10.1109/sita.2015.7358432

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…In this section, a series of simulation experiments for unmanned drone path planning were conducted using the reinforcement learning library, Gym, in Python. The scenarios for the drone path planning problems in the experiments involved the presence of several obstacles, represented as circular threat areas within the environment [8,9] . The experimental parameters were set as follows: learning rate α=0.15, discount factor γ=1.1, decay factor λ=0.5.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Enhanced Q-learning algorithm with eligibility traces for safe path planning of unmanned surface vehicles in dynamic unknown environment

Wu,

Liu,

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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This study explores the application of a path planning algorithm based on Q-learning and eligibility traces in autonomous task execution for Unmanned Surface Vehicles (USVs). The algorithm aims to provide secure path planning for USVs in dynamic unknown environments, taking into account obstacles, potential threats, and multiple constraints. Initially, a detailed Markov Decision Process (MDP) model was designed. Subsequently, the introduced Q-learning and eligibility trace algorithm demonstrated significant advantages in path planning, utilizing the Upper Confidence Bound (UCB) strategy for action selection. Finally, simulation experiment results indicate that, compared to traditional Q-learning methods, the algorithm can more effectively plan paths for USVs, avoid threat areas, and achieve faster convergence.

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Section: Simulation Experimentsmentioning

confidence: 99%

Enhanced Q-learning algorithm with eligibility traces for safe path planning of unmanned surface vehicles in dynamic unknown environment

Wu,

Liu,

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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show abstract

“…The path planning problem has been studied actively since the 1970s [27]. These algorithms can be divided into two main categories: accurate methods and heuristic methods [28,29]. Accurate methods find the globally optimal solution in a limited time and also provides the assurance of its optimality [30].…”

Section: Applicationmentioning

confidence: 99%

Efficient Chaotic Imperialist Competitive Algorithm with Dropout Strategy for Global Optimization

et al. 2020

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The imperialist competitive algorithm combined with chaos theory (CICA) demonstrates excellent performance in global optimization problems. However, its computational complexity increases with the introduction of chaotic maps. To address this, we integrate CICA with a dropout strategy that randomly samples the dimensions of each solution at each iteration of the computation. We investigate the potential of the proposed algorithm with different chaotic maps through six symmetric and six asymmetric benchmark functions. We also apply the proposed algorithm to AUVs’ path planning application showing its performance and effectiveness in solving real problems. The simulation results show that the proposed algorithm not only has low computational complexity but also enhances local search capability near the globally optimal solution with an insignificant loss in the success rate.

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Path Planning of Robotic Fish in Unknown Environment with Improved Reinforcement Learning Algorithm

Mei

Chen

et al. 2018

Internet and Distributed Computing Systems

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Collaborative Q-learning path planning for autonomous robots based on holonic multi-agent system

Cited by 11 publications

References 13 publications

Enhanced Q-learning algorithm with eligibility traces for safe path planning of unmanned surface vehicles in dynamic unknown environment

Enhanced Q-learning algorithm with eligibility traces for safe path planning of unmanned surface vehicles in dynamic unknown environment

Efficient Chaotic Imperialist Competitive Algorithm with Dropout Strategy for Global Optimization

Path Planning of Robotic Fish in Unknown Environment with Improved Reinforcement Learning Algorithm

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