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- Title
A deep reinforcement learning strategy for autonomous robot flocking.
- Authors
Martínez, Fredy; Montiel, Holman; Wanumen, Luis
- Abstract
Social behaviors in animals such as bees, ants, and birds have shown high levels of intelligence from a multi-agent system perspective. They present viable solutions to real-world problems, particularly in navigating constrained environments with simple robotic platforms. Among these behaviors is swarm flocking, which has been extensively studied for this purpose. Flocking algorithms have been developed from basic behavioral rules, which often require parameter tuning for specific applications. However, the lack of a general formulation for tuning has made these strategies difficult to implement in various real conditions, and even to replicate laboratory behaviors. In this paper, we propose a flocking scheme for small autonomous robots that can self-learn in dynamic environments, derived from a deep reinforcement learning process. Our approach achieves flocking independently of population size and environmental characteristics, with minimal external intervention. Our multi-agent system model considers each agent’s action as a linear function dynamically adjusting the motion according to interactions with other agents and the environment. Our strategy is an important contribution toward real-world flocking implementation. We demonstrate that our approach allows for autonomous flocking in the system without requiring specific parameter tuning, making it ideal for applications where there
- Subjects
DEEP reinforcement learning; AUTONOMOUS robots; REINFORCEMENT learning; ANIMAL social behavior; MULTIAGENT systems; INTELLIGENCE levels
- Publication
International Journal of Electrical & Computer Engineering (2088-8708), 2023, Vol 13, Issue 5, p5707
- ISSN
2088-8708
- Publication type
Article
- DOI
10.11591/ijece.v13i5.pp5707-5716