Task Migration Based on Reinforcement Learning in Vehicular Edge Computing

Algorithm 1

Deep Q-learning method.

Initialize main DQN with random weights

Initialize target DQN with weights

Initialize replay memory to capacity

For each episode do

  Initialize initial state , reward

  For time slot , do

   The controller acquires information about vehicles, tasks, and VECS by interacting

   with the environment

   If the random number < :

    Select action

   Else:

    Randomly select action

   Execute action at controller, observe reward and next state

   Store the tuple in

   Randomly sample a minibatch of tuple from

   If episode terminates at , then

   Else:

   Perform a gradient descent step on with respect to the network parameters

    where loss function is and update

   Terminate when all the vehicles are out of simulation region

  End for

End for