… joint resource optimization problem in the virtualized vehicular networks. We exploit deep
reinforcement learning … ence levels, and the resource allocation and scheduling solution in this …

… the feasibility of using online RL to learn resource valuation estimates (in lookup table
form) which can be used to make high-quality server allocation decisions in a multi-application …

… methodology of reinforcement learning in conjunction with … of existing resource allocation
policies and learn the values … of learning U i ( s ) in the dynamic resource allocation setting. …

… reinforcement learning (RL) techniques for the important problem of optimal online allocation
of resources… Data Center scenario for studying resource allocation, which generally follows …

… introduce the reinforcement learning method and several strategies adopted in our proposed
resource allocation algorithm DRLRA. Then, we present the classical Q-learning and the …

… a learning agent and each resource allocation solution corresponds to an action taken by the
UAVs. Afterwards, we develop a multi-agent reinforcement learning (… using learning. More …

… However, resource allocation … Reinforcement Learning (DRL) on solving complicated control
problems, we present a novel DRL-based framework for power-efficient resource allocation …

… However, the MEC system usually only has the limited virtual resources, which are … a
resource allocation policy for the IoT edge computing system to improve the efficiency of resource …

… for resource allocation in CPS, which was based on a design of the reinforcement learning
… service level, which enabled a dynamic resource allocation and avoided a fixed input task …

… 342–343 (2005)) we showed the feasibility of using online RL to learn resource valuation
estimates (in lookup table form) which can be used to make high-quality server allocation …