Universal Artificial Pheromone Framework with Deep Reinforcement Learning for Robotic Systems

Na, Seongin; Niu, Hongmei; Lennox, Barry; Arvin, Farshad

doi:10.1109/iccre51898.2021.9435666

Cited by 7 publications

(10 citation statements)

References 13 publications

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“…Through the experiments, the suitability of the PhERS framework is also validated. Compared to our previous work [34] that first validated the PhERS framework, the experiments conducted in this study include more complex environments, use of a more advanced DRL-based controllers and an additional metric for more elaborate analysis so that the benefits of the proposed framework and the improved version of DRL-based controller could be further investigated.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning

Niu

Lennox

et al. 2022

IEEE Trans. Veh. Technol.

Self Cite

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Section: Methodsmentioning

confidence: 99%

“…Likewise, the communication network sends pheromone information from the agents to the main PhERS controller so that the released pheromone data is applied to the Phero-grids. A basic version of PhERS was tested for a simple environment scenario in [34].…”

Section: Artificial Pheromone Frameworkmentioning

confidence: 99%

Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning

Niu

Lennox

et al. 2022

IEEE Trans. Veh. Technol.

Self Cite

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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%

“…Ground Robots Manipulators [23,24,38,46,53,56,57,68,76,83,85,91,104,106,110,110,120,123,134,135,138,139,141,142,161,167,171,172,186,205,207,[218][219][220]230,240,269] [ 22,39,49,52,54,55,59,70,71,[73][74][75][77][78][79]…”

Section: Aerial Robotsmentioning

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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“…Although the feasibility and benefits of RL have been demonstrated and expected to produce more promising results as it develops, there is a critical issue in implementing RL for swarm robotic systems in real-world applications. In the simulated environments, all data collected from individuals are used for training in a central server [16]- [18]. This dependence in a central server is also shown with Multi-Agent Reinforcement Learning (MARL) methods, which is a sub-domain of RL dealing with multi-agent problem.…”

Section: Introductionmentioning

confidence: 99%

Federated Reinforcement Learning for Collective Navigation of Robotic Swarms

Na¹,

Krajník²,

Lennox³

et al. 2022

Preprint

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The recent advancement of Deep Reinforcement Learning (DRL) contributed to robotics by allowing automatic controller design. Automatic controller design is a crucial approach for designing swarm robotic systems, which require more complex controller than a single robot system to lead a desired collective behaviour. Although DRL-based controller design method showed its effectiveness, the reliance on the central training server is a critical problem in the real-world environments where the robot-server communication is unstable or limited. We propose a novel Federated Learning (FL) based DRL training strategy for use in swarm robotic applications. As FL reduces the number of robot-server communication by only sharing neural network model weights, not local data samples, the proposed strategy reduces the reliance on the central server during controller training with DRL. The experimental results from the collective learning scenario showed that the proposed FL-based strategy dramatically reduced the number of communication by minimum 1600 times and even increased the success rate of navigation with the trained controller by 2.8 times compared to the baseline strategies that share a central server. The results suggest that our proposed strategy can efficiently train swarm robotic systems in the real-world environments with the limited robot-server communication, e.g. agri-robotics, underwater and damaged nuclear facilities.

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Universal Artificial Pheromone Framework with Deep Reinforcement Learning for Robotic Systems

Cited by 7 publications

References 13 publications

Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning

Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning

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

Federated Reinforcement Learning for Collective Navigation of Robotic Swarms

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