Add to ORKGAbstract This paper covers area of Collective Reinforcement Learning. We introduce and describe new simple approach to Collective Reinforcement Learning named Related Temporal Difference. This approach can supports coherence of agent's behavior in distributed and structurally complicated multi-agent system. We construct a decentralized Multi-Agent system which describes behaviors of multi-joint robot. Given experiments show, that system of local learning procedures in complex system can be much faster than learning system on the whole
In this paper we focus on the problem of designing a collective of autonomous agents that individual...
In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation ...
International audienceMulti-agent systems (MAS) are a field of study of growing interest in a variet...
This paper describes a multi-agent influence learning approach and reinforcement learning adaptatio...
This paper describes a multi-agent influence learning approach and reinforcement learning adaptatio...
In this work would be considered multiagent approach to solve intellectual tasks based on Reinforc...
Reinforcement Learning has established as a framework that allows an autonomous agent for automatica...
Reinforcement Learning has established as a framework that allows an autonomous agent for automatica...
Abstract Multi-agent systems are rapidly nding applications in a variety of domains, including robo...
peer reviewedIn this article we describe a set of scalable techniques for learning the behavior of a...
<p>A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individua...
Multi-Agent systems naturally arise in a variety of domains such as robotics, distributed control an...
In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation ...
This paper is devoted to the problem of reinforcement learning in multi-agent systems. Multi-agent s...
This paper is devoted to the problem of reinforcement learning in multi-agent systems. Multi-agent s...
In this paper we focus on the problem of designing a collective of autonomous agents that individual...
In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation ...
International audienceMulti-agent systems (MAS) are a field of study of growing interest in a variet...
This paper describes a multi-agent influence learning approach and reinforcement learning adaptatio...
This paper describes a multi-agent influence learning approach and reinforcement learning adaptatio...
In this work would be considered multiagent approach to solve intellectual tasks based on Reinforc...
Reinforcement Learning has established as a framework that allows an autonomous agent for automatica...
Reinforcement Learning has established as a framework that allows an autonomous agent for automatica...
Abstract Multi-agent systems are rapidly nding applications in a variety of domains, including robo...
peer reviewedIn this article we describe a set of scalable techniques for learning the behavior of a...
<p>A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individua...
Multi-Agent systems naturally arise in a variety of domains such as robotics, distributed control an...
In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation ...
This paper is devoted to the problem of reinforcement learning in multi-agent systems. Multi-agent s...
This paper is devoted to the problem of reinforcement learning in multi-agent systems. Multi-agent s...
In this paper we focus on the problem of designing a collective of autonomous agents that individual...
In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation ...
International audienceMulti-agent systems (MAS) are a field of study of growing interest in a variet...