Shared communication for coordinated large-scale reinforcement learning control
Deep Reinforcement Learning (DRL) recently emerged as a possibility to control complex systems without the need to model them mathematically. In contrast to classical controllers, DRL alleviates the need for constant parameter tuning, tedious design of control laws, and re-identification procedures...
Main Authors: | Nicolas Bougie, Takashi Onishi, Yoshimasa Tsuruoka |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2023-12-01
|
Series: | SICE Journal of Control, Measurement, and System Integration |
Subjects: | |
Online Access: | http://dx.doi.org/10.1080/18824889.2023.2174647 |
Similar Items
-
Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control
by: Rohit Bokade, et al.
Published: (2023-01-01) -
Optimal control and reinforcement learning for formula one lap simulation
by: Hoeppke, C
Published: (2022) -
A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET
by: Xiaofeng Liu, et al.
Published: (2023-03-01) -
Towards Real-Time Reinforcement Learning Control of a Wave Energy Converter
by: Enrico Anderlini, et al.
Published: (2020-10-01) -
Coordination and communication in deep multi-agent reinforcement learning
by: Schroeder de Witt, CA
Published: (2021)