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Reinforcement learning agents need exploratory behaviors to escape from localoptima. These behaviors may include both immediate dithering perturbation andtemporally consistent exploration. To achieve these, a stochastic policy modelthat is inherently consistent through a period of time is in desire, especiallyfor tasks with either sparse rewards or long term information. In this work, weintroduce a novel on-policy temporally consistent exploration strategy - NeuralAdaptive Dropout Policy Exploration (NADPEx) - for deep reinforcement learningagents. Modeled as a global random variable for conditional distribution,dropout is incorporated to reinforcement learning policies, equipping them withinherent temporal consistency, even when the reward signals are sparse. Twofactors, gradients' alignment with the objective and KL constraint in policyspace, are discussed to guarantee NADPEx policy's stable improvement. Ourexperiments demonstrate that NADPEx solves tasks with sparse reward while naiveexploration and parameter noise fail. It yields as well or even fasterconvergence in the standard mujoco benchmark for continuous control.

NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning