Hani Al-Dayaa

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Reinforcement learning techniques like the QLearning one and the Multiple-Lookahead-Levels one that we introduced in our prior work require the agent to complete an initial exploratory path followed by as many hypothetical and physical paths as necessary to find the optimal path to the goal. This paper introduces a reinforcement learning technique that uses(More)
Reinforcement learning techniques like the Q-Learning one as well as the Multiple-Lookahead-Levels one that we introduced in our prior work require the agent to complete an initial exploratory path followed by as many hypothetical and physical paths as necessary to find the optimal path to the goal. This paper introduces a reinforcement learning technique(More)
Reinforcement learning (RL) techniques have contributed and continue to tremendously contribute to the advancement of machine learning and its many related recent applications. As it is well known, some of the main limitations of existing RL techniques are, in general, their slow convergence and their computational complexity. The contributions of this(More)
Abstract – Reinforcement learning techniques have contributed and continue to tremendously contribute to the advancement of machine learning. In this paper, we introduce a technique for reinforcement learning using multiple lookahead levels that grants an autonomous agent more visibility in its environment and helps it learn faster. This technique extends(More)
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