  Reinforcement learning agents need exploratory behaviors to escape from local
optima. These behaviors may include both immediate dithering perturbation and
temporally consistent exploration. To achieve these, a stochastic policy model
that is inherently consistent through a period of time is in desire, especially
for tasks with either sparse rewards or long term information. In this work, we
introduce a novel on-policy temporally consistent exploration strategy - Neural
Adaptive Dropout Policy Exploration (NADPEx) - for deep reinforcement learning
agents. Modeled as a global random variable for conditional distribution,
dropout is incorporated to reinforcement learning policies, equipping them with
inherent temporal consistency, even when the reward signals are sparse. Two
factors, gradients' alignment with the objective and KL constraint in policy
space, are discussed to guarantee NADPEx policy's stable improvement. Our
experiments demonstrate that NADPEx solves tasks with sparse reward while naive
exploration and parameter noise fail. It yields as well or even faster
convergence in the standard mujoco benchmark for continuous control.
