… Internet of Things (IoT) in current and future networking applications by deploying a diversity
of wireless-… have applied Reinforcement Learning (RL) and Deep Reinforcement Learning (…

… The recent advances in deep reinforcement learning (DRL) algorithms can potentially
address the above problems of IoT systems. In this context, this paper provides a comprehensive …

… through the deep reinforcement learning algorithm. In this algorithm, Deep Q-Network is …
in order to optimize the system performance, and a neural network (NN) is trained to predict the …

… To accelerate the exploration process and find near-optimal sampling locations for the
mobile sensors, deep reinforced exploring learning tree (DRLT) is designed and outperforms …

… As such, this paper proposes Deep Wireless Embedded Reinforcement Learning (… gap
between theoretical and system-level aspects of wireless DRL in the IoT landscape. The key and …

… To address the complex and dynamic control issues, we propose a federated deep-reinforcement-learning-based
cooperative edge caching (FADE) framework. FADE enables base …

… reinforcement learning formulation, and a novel coordinated multiagent deep-reinforcement-…
radio sensor networks for Internet of Things,” IEEE Internet Things J., vol. 5, no. 4, pp. …

… In this paper, we advocate the use of deep reinforcement learning (DRL) to solve the
problem of caching IoT data at the edge without knowing future IoT data popularity, user request …

… , we propose a deep reinforcement learningbased dynamic … Our DDRM algorithm exploits
the deep deterministic policy … Internet of things under active attacks,” IEEE Internet Things J…

… In this paper, we have proposed a new Deep Reinforcement Learning (DRL)-based IDS for
… our scheme against the baseline benchmark of standard reinforcement learning (RL) and the …