… access policy based on deep reinforcement learning algorithm to optimally select the channel
… Thus, the fully distributed reinforcement learning will be explored for channel access in our …

… To overcome the unknown dynamics of network, we model the problem as a sequential …
, and apply deep reinforcement learning (DRL), which aggregates reinforcement learning (RL) …

… wireless sensor networks is to enable each node to harvest energy in its environment. To …
A novel energy management algorithm based on reinforcement learning (RLMan) is proposed …

… that change how the network reacts over time. To that … reinforcement learning techniques
to switch the MAC protocol in structured wireless networks according to the ongoing network …

… -wake and task schedulers, in wireless networks, as well as the … scheduling schemes in
wireless networks in order to explore … work topologies and wireless networks, as well as the chal- …

… In this paper, we propose to use reinforcement learning, namely Q-learning algorithm to …
-radio wireless mesh networks. In the proposed mechanism (RLBDR), reinforcement-learning is …

… In this paper, with the aim to design an autonomous wireless video streaming system, we
apply the deep reinforcement learning approach to dynamic resource optimization for wireless …

… adopting offline reinforcement learning [2] for wireless network … suitable for wireless RRM,
because in practice wireless operators … RL to the domain of wireless network optimization. This …

… In Section V, we propose a reinforcement learning approach as the solution of an MDP model
in … Section VI evaluates the performance of our proposed reinforcement learning method. …

… solution for data collection of large-scale wireless sensor networks (WSNs). In this paper, …
reinforcement learning (DRL) technique, pointer networkA* (Ptr-A*), which can efficiently learn …