Recently, deep reinforcement learning (RL) has achieved remarkable empirical success by integrating neural networks into RL frameworks. However, these algorithms often require a large number of training samples and admit little theoretical understanding. To mitigate issues, we propose theoretically principled nearest neighbor (NN) function approximator that can replace the value in methods. Ins...