Multi-Robot Formation Control Using Reinforcement Learning
arXiv:2001.04527
Abstract
In this paper, we present a machine learning approach to move a group of robots in a formation. We model the problem as a multi-agent reinforcement learning problem. Our aim is to design a control policy for maintaining a desired formation among a number of agents (robots) while moving towards a desired goal. This is achieved by training our agents to track two agents of the group and maintain the formation with respect to those agents. We consider all agents to be homogeneous and model them as unicycle [1]. In contrast to the leader-follower approach, where each agent has an independent goal, our approach aims to train the agents to be cooperative and work towards the common goal. Our motivation to use this method is to make a fully decentralized multi-agent formation system and scalable for a number of agents.
References in corpus (7)
- Continuous control with deep reinforcement learning
- Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
- Counterfactual Multi-Agent Policy Gradients
- Hindsight Experience Replay
- Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability
- Loosely Coupled Payload Transport System with Robot Replacement
- Traffic Management Strategies for Multi-Robotic Rigid Payload Transport Systems