  Deep Q-Networks (DQN) is one of the most well-known methods of deep
reinforcement learning, which uses deep learning to approximate the
action-value function. Solving numerous Deep reinforcement learning challenges
such as moving targets problem and the correlation between samples are the main
advantages of this model. Although there have been various extensions of DQN in
recent years, they all use a similar method to DQN to overcome the problem of
moving targets. Despite the advantages mentioned, synchronizing the network
weight in a fixed step size, independent of the agent's behavior, may in some
cases cause the loss of some properly learned networks. These lost networks may
lead to states with more rewards, hence better samples stored in the replay
memory for future training. In this paper, we address this problem from the DQN
family and provide an adaptive approach for the synchronization of the neural
weights used in DQN. In this method, the synchronization of weights is done
based on the recent behavior of the agent, which is measured by a criterion at
the end of the intervals. To test this method, we adjusted the DQN and rainbow
methods with the proposed adaptive synchronization method. We compared these
adjusted methods with their standard form on well-known games, which results
confirm the quality of our synchronization methods.
