Reinforcement learning and multi-agent control within an internet environment

2011 ◽  
pp. 327-348
Author(s):  
P. Tillotson ◽  
Q. Wu ◽  
P. Hughes
Keyword(s):  
Reinforcement Learning ◽  
Multi Agent ◽  
Internet Environment ◽  
Agent Control

scholarly journals A Multi-Agent Control Architecture for a Robotic Wheelchair

2006 ◽  
Vol 3 (3) ◽  
pp. 179-189 ◽  
Author(s):  
C. Galindo ◽  
A. Cruz-Martin ◽  
J. L. Blanco ◽  
J. A. Fernńndez-Madrigal ◽  
J. Gonzalez
Keyword(s):  
Reinforcement Learning ◽  
Control Architecture ◽  
Daily Lives ◽  
Multi Agent ◽  
Human Skills ◽  
Overall Performance ◽  
Agent Control ◽  
Robotic Wheelchairs ◽  
Assistant Robots ◽  
Robotic Wheelchair

Assistant robots like robotic wheelchairs can perform an effective and valuable work in our daily lives. However, they eventually may need external help from humans in the robot environment (particularly, the driver in the case of a wheelchair) to accomplish safely and efficiently some tricky tasks for the current technology, i.e. opening a locked door, traversing a crowded area, etc. This article proposes a control architecture for assistant robots designed under a multi-agent perspective that facilitates the participation of humans into the robotic system and improves the overall performance of the robot as well as its dependability. Within our design, agents have their own intentions and beliefs, have different abilities (that include algorithmic behaviours and human skills) and also learn autonomously the most convenient method to carry out their actions through reinforcement learning. The proposed architecture is illustrated with a real assistant robot: a robotic wheelchair that provides mobility to impaired or elderly people.

scholarly journals Knowledge-Guided Agent-Tactic-Aware Learning for StarCraft Micromanagement

2018 ◽  
Author(s):  
Yue Hu ◽  
Juntao Li ◽  
Xi Li ◽  
Gang Pan ◽  
Mingliang Xu
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Learning Strategy ◽  
State Of The Art ◽  
Game Playing ◽  
Challenging Problem ◽  
Two Stage ◽  
Exploration Process ◽  
Multi Agent ◽  
Agent Control

As an important and challenging problem in artificial intelligence (AI) game playing, StarCraft micromanagement involves a dynamically adversarial game playing process with complex multi-agent control within a large action space. In this paper, we propose a novel knowledge-guided agent-tactic-aware learning scheme, that is, opponent-guided tactic learning (OGTL), to cope with this micromanagement problem. In principle, the proposed scheme takes a two-stage cascaded learning strategy which is capable of not only transferring the human tactic knowledge from the human-made opponent agents to our AI agents but also improving the adversarial ability. With the power of reinforcement learning, such a knowledge-guided agent-tactic-aware scheme has the ability to guide the AI agents to achieve high winning-rate performances while accelerating the policy exploration process in a tactic-interpretable fashion. Experimental results demonstrate the effectiveness of the proposed scheme against the state-of-the-art approaches in several benchmark combat scenarios.

scholarly journals Scalable Reinforcement Learning Policies for Multi-Agent Control

2021 ◽  
Author(s):  
Christopher D. Hsu ◽  
Heejin Jeong ◽  
George J. Pappas ◽  
Pratik Chaudhari
Keyword(s):  
Reinforcement Learning ◽  
Multi Agent ◽  
Agent Control ◽  
Learning Policies

Multi-agent Control of Customer^|^apos;s Loads for Influence of Renewable Energy on the Power System

2014 ◽  
Vol 134 (10) ◽  
pp. 1515-1523
Author(s):  
Akihiro Ogawa ◽  
Kazunari Maki ◽  
Kiyoshi Hata ◽  
Yasunori Takeuchi ◽  
Fumio Ishikawa
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Multi Agent ◽  
Agent Control

Multi-agent deep reinforcement learning with type-based hierarchical group communication

2021 ◽  
Author(s):  
Hao Jiang ◽  
Dianxi Shi ◽  
Chao Xue ◽  
Yajie Wang ◽  
Gongju Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Group Communication ◽  
Multi Agent ◽  
Hierarchical Group
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