Safe Multi-Agent Reinforcement Learning for Formation Control without Individual Reference Targets
CoRR(2023)
摘要
In recent years, formation control of unmanned vehicles has received
considerable interest, driven by the progress in autonomous systems and the
imperative for multiple vehicles to carry out diverse missions. In this paper,
we address the problem of behavior-based formation control of mobile robots,
where we use safe multi-agent reinforcement learning~(MARL) to ensure the
safety of the robots by eliminating all collisions during training and
execution. To ensure safety, we implemented distributed model predictive
control safety filters to override unsafe actions. We focus on achieving
behavior-based formation without having individual reference targets for the
robots, and instead use targets for the centroid of the formation. This
formulation facilitates the deployment of formation control on real robots and
improves the scalability of our approach to more robots. The task cannot be
addressed through optimization-based controllers without specific individual
reference targets for the robots and information about the relative locations
of each robot to the others. That is why, for our formulation we use MARL to
train the robots. Moreover, in order to account for the interactions between
the agents, we use attention-based critics to improve the training process. We
train the agents in simulation and later on demonstrate the resulting behavior
of our approach on real Turtlebot robots. We show that despite the agents
having very limited information, we can still safely achieve the desired
behavior.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要