This paper shows that the competitive learning rule found in Learning Vector Quantization (LVQ) serves as a promising function approximator to enable reinforcement learning methods to cope with a large decision search space, defined in terms of different classes of input patterns, like those found in the game of Go. In particular, this paper describes S[arsa]LVQ, a novel reinforcement learning ...

This paper introduces a novel Q-value based adaptive call admission control scheme (Q-CAC) for distributed reinforcement learning (RL) based dynamic spectrum access (DSA) in mobile cellular networks, which provides a good quality of service (QoS) without the need for spectrum sensing. A DSA algorithm has been developed in this paper using the stateless Q-learning algorithm with Win-or-Learn-Fas...

One fundamental issue in multiagent reinforcement learning is how to deal with the limited local knowledge of an agent in order to achieve effective learning. In this paper, we argue that this issue can be more effectively solved if agents are equipped with a consistent global view. We achieve this by requiring agents to follow an interacting protocol. The properties of the protocol are derived...

This work introduces a novel approach for solving reinforcement learning problems in multi-agent settings. We propose a state reformulation of multi-agent problems in R that allows the system state to be represented in an image-like fashion. We then apply deep reinforcement learning techniques with a convolution neural network as the Q-value function approximator to learn distributed multi-agen...

the main challenge of a search engine is ranking web documents to provide the best response to a user`s query. despite the huge number of the extracted results for user`s query, only a small number of the first results are examined by users; therefore, the insertion of the related results in the first ranks is of great importance. in this paper, a ranking algorithm based on the reinforcement le...

In the distributed systems in which information cannot be exchanged directly among agents, we deal with problems of deciding how each agent holds the shared resource. To achieve a lot of tasks greedily, agents tend to attempt to hold the resources for a long term. However the system performance decreases consequentially because it competes with the processing of other agents’ tasks. To acquire ...