Abstract- This paper presents a cooperative reinforcement learning algorithm of multi-agent systems. The cooperative behaviour is established within a leader-following framework. Specifically, the cooperative dynamics is modelled as a Stackelberg game. Based on the equilibrium definition of the Stackelberg game, a leader-following Q-learning algorithm is developed. The algorithm is generalised over continuous state space by using fuzzy logic. Index Terms- Reinforcement learning, multi-agent systems, multi-agent reinforcement learning, fuzzy Q-learning
A multiagent reinforcement learning algorithm with fuzzy policy is addressed in this paper for deali...
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We present a distributed variant of Q-learning that allows to learn the optimal cost-to-go function ...
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We report on an investigation of reinforcement learning techniques for the learning of coordination...
This paper presents a policy gradient multi-agent reinforcement learning algorithm for leader-follow...
Research Doctorate - Doctor of Philosophy (PhD)Machine learning in multi-agent domains poses several...
A multi-agent reinforcement learning algorithm with fuzzy policy is addressed in this paper. This al...
We present a conceptual framework for creating Qlearning-based algorithms that converge to optimal e...
A multi-agent system (MAS) is suitable for addressing tasks in a variety of domains without any prog...
We report on an investigation of reinforcement learning tech-niques for the learning of coordination...
Some game theory approaches to solve multiagent reinforce-ment learning in self play, i.e. when agen...
This paper focuses on a multi-agent cooperation which is generally difficult to be achieved without ...
A multiagent reinforcement learning algorithm with fuzzy policy is addressed in this paper for deali...
The book begins with a chapter on traditional methods of supervised learning, covering recursive lea...
We present a distributed variant of Q-learning that allows to learn the optimal cost-to-go function ...
Traditional Reinforcement learning algorithm can only solve the learning problem of the intelligent ...
The hierarchical organisation of distributed systems can provide an efficient decomposition for mach...
One of the important issues in intelligent systems and robotics is to develop an efficient method to...
We report on an investigation of reinforcement learning techniques for the learning of coordination...
This paper presents a policy gradient multi-agent reinforcement learning algorithm for leader-follow...
Research Doctorate - Doctor of Philosophy (PhD)Machine learning in multi-agent domains poses several...
A multi-agent reinforcement learning algorithm with fuzzy policy is addressed in this paper. This al...
We present a conceptual framework for creating Qlearning-based algorithms that converge to optimal e...
A multi-agent system (MAS) is suitable for addressing tasks in a variety of domains without any prog...
We report on an investigation of reinforcement learning tech-niques for the learning of coordination...
Some game theory approaches to solve multiagent reinforce-ment learning in self play, i.e. when agen...
This paper focuses on a multi-agent cooperation which is generally difficult to be achieved without ...
A multiagent reinforcement learning algorithm with fuzzy policy is addressed in this paper for deali...
The book begins with a chapter on traditional methods of supervised learning, covering recursive lea...
We present a distributed variant of Q-learning that allows to learn the optimal cost-to-go function ...