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...

Link prediction is a social network research area that tries to predict future links using network structure. The main approaches in this area are based on predicting future links using network structure at a specific period, without considering the links behavior through different periods. For example, a common traditional approach in link prediction calculates a chosen similarity metric for e...

Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In highdimensional and continuous domains, tile coding with linear function approximation has been widely used to circumvent the curse of dimensionality, but it suffers from the drawback that human-guided identification of features is required to create effective ...

This paper proposes a TD (temporal difference) and GA (genetic algorithm) based reinforcement (TDGAR) neural learning scheme for controlling chaotic dynamical systems based on the technique of small perturbations. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to fulfill the reinforcement learning task. Structurely, the TDGAR learning system i...

This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward net...

In the field of artificial intelligence, one of the hardest things that we can try to make a computer program do is to interact with the real world. In contrast to the well-defined, discrete, simplified world that programs normally operate in, the real world is large, unknown, and complex. In the real world, programs must learn and adapt to new and changing situations in order to be effective. ...

In this paper reinforcement learning with binary vector actions was investigated. We suggest an effective architecture of the neural networks for approximating an action-value function with binary vector actions. The proposed architecture approximates the action-value function by a linear function with respect to the action vector, but is still non-linear with respect to the state input. We sho...

R einforcement learning (RL) is the problem of learning how to act only from interaction and limited reinforcement. An agent takes actions in an unknown environment , observes their effects, and obtains rewards. The agent's aim is to learn how the environment works in order to maximize the total reward obtained during its lifetime. RL problems are quite general. They can subsume almost any arti...

Recently Gerald Ash has shown through case studies that event dependent routing is attractive in large scale multi-service MPLS networks. In this paper, we consider the application of Load Shared Sequential Routing (LSSR) in MPLS networks where the load sharing factors are updated using reinforcement learning techniques. We present algorithms based on learning automata techniques for optimizing...

Reinforcement learning is an approach for learning optimal action policy via experiencing, i.e. using observed reward in environment states. Reinforcement learning algorithms include adaptive dynamic programming, temporal difference learning and Q-learning[1]. Examples of successful applications of reinforcement learning are controller for sustained inverted flight on an autonomous helicopter [...