The development of mechanisms that enable robot teams to autonomously generate cooperative behaviours is one of the most interesting issues in dis- tributed and autonomous robotic systems. In this paper, the application of reinforcement learning techniques to robot teams is studied, enabling the robot to learn cooperative behaviours based only on local information.Publicad
In many applications in robotics, there exist teams of robots operating in dynamic environments requ...
Abstract — In this paper, we propose a reinforcement learning approach to address multi-robot cooper...
Cooperative decentralized multirobot learning refers to the use of multiple learning entities to lea...
The development of mechanisms that enable robot teams to autonomously generate cooperative behaviour...
Reinforcement learning has been widely applied to solve a diverse set of learning tasks, from board ...
Abstract. Reinforcement learning has been widely applied to solve a diverse set of learning tasks, f...
The objective of my research described in this dissertation is to realize learning and evolutionary ...
10.1109/IROS.2005.15451462005 IEEE/RSJ International Conference on Intelligent Robots and Systems, I...
Abstract. This paper addresses the problem of cooperation between learning situated agents. We prese...
We are approaching a future where robots and humans will co-exist and co-adapt. To understand how ca...
This paper proposes a method that acquires the pur-posive behaviors based on the estimation of the s...
Despite the advancement of research and development on multi-robot teams, a key challenge still rema...
As most action generation problems of autonomous robots can be phrased in terms of sequential decisi...
Abstract—Multi-agent systems (MAS) are a field of study of growing interest in a variety of domains ...
<p>A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individua...
In many applications in robotics, there exist teams of robots operating in dynamic environments requ...
Abstract — In this paper, we propose a reinforcement learning approach to address multi-robot cooper...
Cooperative decentralized multirobot learning refers to the use of multiple learning entities to lea...
The development of mechanisms that enable robot teams to autonomously generate cooperative behaviour...
Reinforcement learning has been widely applied to solve a diverse set of learning tasks, from board ...
Abstract. Reinforcement learning has been widely applied to solve a diverse set of learning tasks, f...
The objective of my research described in this dissertation is to realize learning and evolutionary ...
10.1109/IROS.2005.15451462005 IEEE/RSJ International Conference on Intelligent Robots and Systems, I...
Abstract. This paper addresses the problem of cooperation between learning situated agents. We prese...
We are approaching a future where robots and humans will co-exist and co-adapt. To understand how ca...
This paper proposes a method that acquires the pur-posive behaviors based on the estimation of the s...
Despite the advancement of research and development on multi-robot teams, a key challenge still rema...
As most action generation problems of autonomous robots can be phrased in terms of sequential decisi...
Abstract—Multi-agent systems (MAS) are a field of study of growing interest in a variety of domains ...
<p>A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individua...
In many applications in robotics, there exist teams of robots operating in dynamic environments requ...
Abstract — In this paper, we propose a reinforcement learning approach to address multi-robot cooper...
Cooperative decentralized multirobot learning refers to the use of multiple learning entities to lea...
Add to ORKG