… using reinforcement learning approaches and to analyze the performance of the proposed
algorithms. We consider a setup where we want to optimize a possibly nonlinear joint …

… In this paper, we proposed a novel reinforcement learning method, called JAC, to jointly
optimize sensing, decisionmaking and motion-controlling in autonomous driving. On the one …

… jointly, and to avoid the curse of dimensionality caused by the complexity of the action
space, we propose a joint optimization method … based on deep reinforcement learning (JODRBRL)…

… a multi-agent reinforcement learning (MARL) algorithm based on the proximal policy
optimization (PPO) method, by introducing the centralized training with decentralized execution …

… a multi-objective reinforcement learning strategy, called … joint computation offloading and
collaboration is a general framework of the strategy, it first uses the K-nearest neighbor method …

… UAVs joint optimization problems to enhance system efficiency. We classify the joint optimization
problems based on the number of parameters used in proposed optimization problems. …

… using reinforcement learning approaches and to analyze the performance of the proposed
algorithms. We consider a setup where we want to optimize a possibly nonlinear joint …

… Unlike any of the above methods, our proposed method utilizes the powerful data … of deep
reinforcement learning to effectively deal with dynamic environments via training of the system. …

… -aware joint channel and power allocation (SA-JCPA) scheme using a two-step approach. …
and proposed a decentralized deep reinforcement learning method to enhance spectrum …

… optimal stationary policy is presented. This provides the basis for the improvement in R-learning
algorithm. Furthermore, a novel heuristic reinforcement learning method is proposed to …