Add to ORKGIn this paper, we consider multi-agent system in which every agents have own tasks that differs each other. We propose a method that decreases learning time of reinforcement learning by using the model of environment. In the proposed algorithm, the model is created by sharing the experiences of agents each other. To demonstrate the effectiveness of the proposed method, simulations of a puddle world and experiments of a maze world have been carried out. As a result effective behaviors have been obtained quickly.</p
Recently, reinforcement learning has been proposed as an effective method for knowledge acquisition ...
In this paper we propose a method for multi-agent reinforcement learning by automatic discovery of a...
Learning in a partially observable and nonstationary environment is still one of the challenging pro...
In this paper, we consider multi-agent system in which every agents have own tasks that differs each...
Reinforcement learning is one of effective controller for autonomous robots. Because it does not nee...
This paper is devoted to the problem of reinforcement learning in multi-agent systems. Multi-agent s...
Individual learning in an environment where more than one agent exist is a challenging task. In this...
Mutual learning is an emerging field in intelligent systems which takes inspiration from naturally i...
In this paper, we are interested in systems with multiple agents that wish to cooperate in order to ...
Abstract. Training agents in a virtual crowd to achieve a task can be accomplished by allowing the a...
For multi-agent reinforcement learning in Markov games, knowledge extraction and sharing are key res...
AbstractA major concern in multi-agent coordination is how to select algorithms that can lead agents...
This paper focuses on a multi-agent cooperation which is generally difficult to be achieved without ...
The objective of my research described in this dissertation is to realize learning and evolutionary ...
Artificial intelligence algorithms enable autonomous agents to perform sophisticated tasks with grea...
Recently, reinforcement learning has been proposed as an effective method for knowledge acquisition ...
In this paper we propose a method for multi-agent reinforcement learning by automatic discovery of a...
Learning in a partially observable and nonstationary environment is still one of the challenging pro...
In this paper, we consider multi-agent system in which every agents have own tasks that differs each...
Reinforcement learning is one of effective controller for autonomous robots. Because it does not nee...
This paper is devoted to the problem of reinforcement learning in multi-agent systems. Multi-agent s...
Individual learning in an environment where more than one agent exist is a challenging task. In this...
Mutual learning is an emerging field in intelligent systems which takes inspiration from naturally i...
In this paper, we are interested in systems with multiple agents that wish to cooperate in order to ...
Abstract. Training agents in a virtual crowd to achieve a task can be accomplished by allowing the a...
For multi-agent reinforcement learning in Markov games, knowledge extraction and sharing are key res...
AbstractA major concern in multi-agent coordination is how to select algorithms that can lead agents...
This paper focuses on a multi-agent cooperation which is generally difficult to be achieved without ...
The objective of my research described in this dissertation is to realize learning and evolutionary ...
Artificial intelligence algorithms enable autonomous agents to perform sophisticated tasks with grea...
Recently, reinforcement learning has been proposed as an effective method for knowledge acquisition ...
In this paper we propose a method for multi-agent reinforcement learning by automatic discovery of a...
Learning in a partially observable and nonstationary environment is still one of the challenging pro...