Multi-Agent Reinforcement Learning For Swarm Robots Formation
The project discussed the Multi-Agent Reinforcement Learning (MARL) with an idea to the proposed mobile robot which able to follow the line and avoid the obstacle in a given environment. The reinforcement learning algorithm offers one of the most general frameworks in learning subjects to address so...
Saved in:
| Main Author: | |
|---|---|
| Format: | Monograph |
| Language: | English |
| Published: |
Universiti Sains Malaysia
2021
|
| Subjects: | |
| Online Access: | http://eprints.usm.my/54499/1/Multi-Agent%20Reinforcement%20Learning%20For%20Swarm%20Robots%20Formation.pdf http://eprints.usm.my/54499/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.usm.eprints.54499 |
|---|---|
| record_format |
eprints |
| spelling |
my.usm.eprints.54499 http://eprints.usm.my/54499/ Multi-Agent Reinforcement Learning For Swarm Robots Formation Bujang, Christina T Technology The project discussed the Multi-Agent Reinforcement Learning (MARL) with an idea to the proposed mobile robot which able to follow the line and avoid the obstacle in a given environment. The reinforcement learning algorithm offers one of the most general frameworks in learning subjects to address some of the control issues in a multi-agent system. The mobile robot is an independent agent that can use sensors, actuators, and control techniques to navigate intelligently based on the specific task required. Specifically, reinforcement learning is employed for developing the training process for the mobile robot to reach the given task as it needs to learn by itself to follow the black line and avoid the obstacle in a given environment based on this project proposed. The reinforcement learning approach presents the algorithm for MARL in a cooperative problem to improve control performance. Experimental and simulation will be carried out to validate the results of the multi-agent control performance. Hence, it should be easy to observe if the control performance shows improvement after learning and can achieve the project proposed. The experiment will therefore indicate the results of the simulation and apply it to the real-time environment as proposed by the project. Universiti Sains Malaysia 2021-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/54499/1/Multi-Agent%20Reinforcement%20Learning%20For%20Swarm%20Robots%20Formation.pdf Bujang, Christina (2021) Multi-Agent Reinforcement Learning For Swarm Robots Formation. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Aeroangkasa. (Submitted) |
| institution |
Universiti Sains Malaysia |
| building |
Hamzah Sendut Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Sains Malaysia |
| content_source |
USM Institutional Repository |
| url_provider |
http://eprints.usm.my/ |
| language |
English |
| topic |
T Technology |
| spellingShingle |
T Technology Bujang, Christina Multi-Agent Reinforcement Learning For Swarm Robots Formation |
| description |
The project discussed the Multi-Agent Reinforcement Learning (MARL) with an idea to the proposed mobile robot which able to follow the line and avoid the obstacle in a given environment. The reinforcement learning algorithm offers one of the most general frameworks in learning subjects to address some of the control issues in a multi-agent system. The mobile robot is an independent agent that can use sensors, actuators, and control techniques to navigate intelligently based on the specific task required. Specifically, reinforcement learning is employed for developing the training process for the mobile robot to reach the given task as it needs to learn by itself to follow the black line and avoid the obstacle in a given environment based on this project proposed. The reinforcement learning approach presents the algorithm for MARL in a cooperative problem to improve control performance. Experimental and simulation will be carried out to validate the results of the multi-agent control performance. Hence, it should be easy to observe if the control performance shows improvement after learning and can achieve the project proposed. The experiment will therefore indicate the results of the simulation and apply it to the real-time environment as proposed by the project. |
| format |
Monograph |
| author |
Bujang, Christina |
| author_facet |
Bujang, Christina |
| author_sort |
Bujang, Christina |
| title |
Multi-Agent Reinforcement Learning For Swarm Robots Formation |
| title_short |
Multi-Agent Reinforcement Learning For Swarm Robots Formation |
| title_full |
Multi-Agent Reinforcement Learning For Swarm Robots Formation |
| title_fullStr |
Multi-Agent Reinforcement Learning For Swarm Robots Formation |
| title_full_unstemmed |
Multi-Agent Reinforcement Learning For Swarm Robots Formation |
| title_sort |
multi-agent reinforcement learning for swarm robots formation |
| publisher |
Universiti Sains Malaysia |
| publishDate |
2021 |
| url |
http://eprints.usm.my/54499/1/Multi-Agent%20Reinforcement%20Learning%20For%20Swarm%20Robots%20Formation.pdf http://eprints.usm.my/54499/ |
| _version_ |
1744354432294846464 |
| score |
13.211869 |