Multi-vehicle Flocking Control with Deep Deterministic Policy Gradient Method

By Yang Lyu, Quan Pan, Jin-wen Hu, Chunhui Zhao, Shuai Liu. 2018

36 citations recorded by the source corpus.

Flocking control has been studied extensively along with the wide application of multi-vehicle systems. In this paper the Multi-vehicles System (MVS) flocking control with collision avoidance and communication preserving is considered based on the deep reinforcement learning framework. Specifically the deep deterministic policy gradient (DDPG) with centralized training and distributed execution process is implemented to obtain the flocking control policy. First, to avoid the dynamically changed observation of state, a three layers tensor based representation of the observation is used so that the state remains constant although the observation dimension is changing. A reward function is designed to guide the way-points tracking, collision avoidance and communication preserving. The reward function is augmented by introducing the local reward function of neighbors. Finally, a centralized training process which trains the shared policy based on common training set among all agents. The proposed method is tested under simulated scenarios with different setup.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

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Citations in the corpus

RODE: Learning Roles to Decompose Multi-Agent Tasks (2020, background): “Cooperative multi-agent problems are ubiquitous in real-world applications, such as crewless aerial vehicles (Pham et al., 2018; Xu et al., 2018) and sensor networks (Zhang & Lesser, 2013).”
Multi-Agent Common Knowledge Reinforcement Learning (2018, background): “micromanagement. 1 Introduction Cooperative multi-agent problems are ubiquitous, for example, in the coordination of autonomous cars (Cao et al., 2013) or unmanned aerial vehicles (Pham et al., 2018; Xu et al., 2018). However, how to learn control policies for such systems remains a major open quest”