… In this work, we design a task computation offloading model in a heterogeneous vehicular
… propose an adaptive computation offloading method based on deep reinforcement learning (…

… a common practice in service computing. With the rapid … based on Deep Reinforcement
Learning (DRL) for adaptive and large-… is adopted to improve reinforcement learning, which can …

… To further reduce the complexity, we propose an adaptive … a deep reinforcement learning-based
online offloading (DROO) framework to maximize the weighted sum of the computation …

… a deep reinforcement learning technique to solve this problem. [20] jointly optimizes the
offloading decision and computational resource allocation. In [21], a computation offloading and …

… His current research interests include deep reinforcement learning, computational intelligence,
adaptive dynamic programming, autonomous driving, robotics, intelligent transportation …

… Using a deep reinforcement learning (RL) approach, we train policy networks for AMR … does
it require a pre-computed training dataset. The local nature of our deep RL (DRL) allows the …

… At each layer except input layer, we compute the input to each unit, as the … After computations
flow forward from input to output, at output layer and each hidden layer, we can compute …

… To alleviate the dilemma, this work aims to use deep reinforcement learning to assist both
the credit assignment and operator selection, where some basic concepts of reinforcement …

… in a mirrored edge computing system, while distributively scheduling computation task
offloading and edge resource allocation in an multiagent deep reinforcement learning approach. …

… With the objectives of minimizing the overall system cost and adapting to the dynamic …
Furthermore, we propose adaptive n-step learning and combine deep neuroevolution with …