Hybrid Formation Control for Multi-Robot Hunters Based on Multi-Agent  Deep Deterministic Policy Gradient

Hamed, Oussama; Hamlich, Mohamed

doi:10.13164/mendel.2021.2.023

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Policy Gradients DDPG PPO Other [23,24,39,40,59, (QMIX), [38] (DDQN), [93] (DDQN), [94] (DDQN), [95] (DQN), [96] (DQN) [46][47][48][49][50][97][98][99][100][101][102][103][104][105][106], [22,[52][53][54][55][56][57] [86,88,129-140] (TRPO), [141] (TRPO), [81] (TRPO), [142] (TD3), [143] (SAC), [144] (SAC)…”

Section: Q-networkmentioning

confidence: 99%

Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey

Orr

Dutta

2023

Sensors

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Deep reinforcement learning has produced many success stories in recent years. Some example fields in which these successes have taken place include mathematics, games, health care, and robotics. In this paper, we are especially interested in multi-agent deep reinforcement learning, where multiple agents present in the environment not only learn from their own experiences but also from each other and its applications in multi-robot systems. In many real-world scenarios, one robot might not be enough to complete the given task on its own, and, therefore, we might need to deploy multiple robots who work together towards a common global objective of finishing the task. Although multi-agent deep reinforcement learning and its applications in multi-robot systems are of tremendous significance from theoretical and applied standpoints, the latest survey in this domain dates to 2004 albeit for traditional learning applications as deep reinforcement learning was not invented. We classify the reviewed papers in our survey primarily based on their multi-robot applications. Our survey also discusses a few challenges that the current research in this domain faces and provides a potential list of future applications involving multi-robot systems that can benefit from advances in multi-agent deep reinforcement learning.

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Section: Q-networkmentioning

confidence: 99%

Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey

Orr

Dutta

2023

Sensors

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“…Path planning methods are often encountered in robotics [10,8,12], video games [33], and many other artificial intelligence applications [22,5]. Their vast utilization and popularity are widely recognized [1,23].…”

Section: Introductionmentioning

confidence: 99%

Explanation and Speedup Comparison of Advanced Path-planning Algorithms Presented on Two-dimensional Grid

Soustek

Matoušek

Dvořák

et al. 2022

mendel

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Path planning or network route planning problems are an important issue in AI, robotics, or computer games. Appropriate implementation and knowledge of advanced and classical path-planning algorithms can be important for both autonomous navigation systems and computer games. In this paper, we compare advanced path planning algorithms implemented on a two-dimensional grid. Advanced path planning algorithms, including pseudocode, are introduced. The experiments were performed in the Python environment, thus with a significant performance margin over C++ or Rust implementations. The main focus is on the speedup of the algorithms compared to a baseline method, which was chosen to be the well-known Dijkstra's algorithm. All experiments correspond to trajectories on a two-dimensional grid, with variously defined constraints. The motion from each node corresponds to a Moore neighborhood, i.e., it is possible in eight directions. In this paper, three well-known path planning algorithms are described and compared: the Dijkstra, A* and A* /w Bounding Box. And two advanced methods are included, namely Jump Point Search (JPS), incorporated with the Bounding Box variant (JPS+BB), and Simple Subgoal (SS). These advanced methods clearly show their advantage in the context of the speed up of solution time.

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Navigation method for autonomous mobile robots based on ROS and multi-robot improved Q-learning

Hamed,

Hamlich

2024

Prog Artif Intell

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Hybrid Formation Control for Multi-Robot Hunters Based on Multi-Agent Deep Deterministic Policy Gradient

Cited by 4 publications

References 10 publications

Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey

Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey

Explanation and Speedup Comparison of Advanced Path-planning Algorithms Presented on Two-dimensional Grid

Navigation method for autonomous mobile robots based on ROS and multi-robot improved Q-learning

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