Decentralized Multi-Robot Formation Control Using Reinforcement Learning

2023-06-26 · Juraj Obradovic, Marko Krizmancic, Stjepan Bogdan

This paper presents a decentralized leader-follower multi-robot formation control based on a reinforcement learning (RL) algorithm applied to a swarm of small educational Sphero robots. Since the basic Q-learning method is known to require large memory resources for Q-tables, this work implements the Double Deep Q-Network (DDQN) algorithm, which has achieved excellent results in many robotic problems. To enhance the system behavior, we trained two different DDQN models, one for reaching the formation and the other for maintaining it. The models use a discrete set of robot motions (actions) to adapt the continuous nonlinear system to the discrete nature of RL. The presented approach has been tested in simulation and real experiments which show that the multi-robot system can achieve and maintain a stable formation without the need for complex mathematical models and nonlinear control laws.

📄 PDF Abstract BibTeX arXiv:2306.14489

Code (0)

등록된 구현이 없습니다.

Tasks

Q-Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Methods 이 논문이 사용한 방법론

Q-Learning Q-Learning is an off-policy temporal difference control algorithm: $$Q\left(S\_{t}, A\_{t}\right) \leftarrow Q\left(S\_{t}, A\_{t}\right) + \alpha\left[R_{t+1} +…

Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning

2021-03-09 · Tianhao Zhang, Yueheng Li, Shuai Li, Qiwei Ye 외

In this paper, the circle formation control problem is addressed for a group of cooperative underactuated fish-like robots involving unknown nonlinear dynamics and disturbances. Based on the reinforcement learning and co…

reinforcement-learning Reinforcement Learning (RL)

Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control

2020-09-20 · Qingrui Zhang, Hao Dong, Wei Pan

Decentralized multi-agent control has broad applications, ranging from multi-robot cooperation to distributed sensor networks. In decentralized multi-agent control, systems are complex with unknown or highly uncertain dy…

Deep Reinforcement Learning Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Multi-Robot Formation Control Using Reinforcement Learning

2020-01-13 · Abhay Rawat, Kamalakar Karlapalem

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 …

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod Robot

2020-05-21 · Malte Schilling, Kai Konen, Frank W. Ohl, Timo Korthals

Locomotion is a prime example for adaptive behavior in animals and biological control principles have inspired control architectures for legged robots. While machine learning has been successfully applied to many tasks i…

continuous-control Continuous Control Deep Reinforcement Learning reinforcement-learning+2

Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net

2019-09-19 · Yuchen Xiao, Joshua Hoffman, Tian Xia, Christopher Amato

In many real-world multi-robot tasks, high-quality solutions often require a team of robots to perform asynchronous actions under decentralized control. Decentralized multi-agent reinforcement learning methods have diffi…

Multi-agent Reinforcement Learning Reinforcement Learning