This paper studies the potential of a novel approach to ensure more efficient and intelligent assignment of capacity through medium access control (MAC) in practical wireless sensor networks. Q-Learning is employed as an intelligent transmission strategy. We review the existing MAC protocols in the context of Q-learning. A recently-proposed, ALOHA and Q-Learning based MAC scheme, ALOHA-Q, is co...

Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-wo...

Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper we attempt to train and combine the base classifiers using an adaptive policy. This policy is learnt through a Q-learning inspired technique. Its effectiveness for an essentially supervised task is demonstrated by experimental results...

A group of cooperative and homogeneous Q-learning agents can cooperate to learn faster and gainmore knowledge. In order to do so, each learner agent must be able to evaluate the expertness and the intelligence level of the other agents, and to assess the knowledge and the information it gets from them. In addition, the learner needs a suitable method to properly combine its own knowledge and wh...

RÉSUMÉ. Cet article présente les résultats expérimentaux obtenus avec une architecture originale permettant un apprentissage générique dans le cadre de processus décisionnels de Markov factorisés observables dans le désordre (PDMFOD). L’article décrit tout d’abord le cadre formel des PDMFOD puis le fonctionnement de l’algorithme, notamment le principe de parallélisation et l’attribution dynamiq...

In this paper, we propose Q-learning with adaptive state segmentation (QLASS). QLASS provides an e cient method to construct state space suitable for Q-learning to accomplish the task in a continuous sensor space. In QLASS, the robot starts with single state covering whole sensor space. The sensor space is segmented incrementally based on sensor vectors and reinforcement signals. The segmented ...

Temporal difference algorithms perform well on discrete and small problems. This paper proposes a modification of the Q-learning algorithm towards natural ability to receive a feature list instead of an already identified state in the input. Complete observability is still assumed. The algorithm, Naive Augmenting Q-Learning, has been designed through building a hierarchical structure of input f...

Deep reinforcement learning (RL) is achieving significant success in various applications like control, robotics, games, resource management, and scheduling. However, the important problem of emergency evacuation, which clearly could benefit from RL, has been largely unaddressed. Indeed, evacuation a complex task that difficult to solve with RL. An situation highly dynamic, lot changing variabl...

Automatic feature learning algorithms are at the forefront of modern day machine learning research. We present a novel algorithm, supervised Q-walk, which applies Q-learning to generate random walks on graphs such that the walks prove to be useful for learning node features suitable for tackling with the node classification problem. We present another novel algorithm, k-hops neighborhood based ...