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The class of deep deterministic off-policy algorithms is effectively appliedto solve challenging continuous control problems. However, current approachesuse random noise as a common exploration method that has several weaknesses,such as a need for manual adjusting on a given task and the absence ofexploratory calibration during the training process. We address thesechallenges by proposing a novel guided exploration method that uses adifferential directional controller to incorporate scalable exploratory actioncorrection. An ensemble of Monte Carlo Critics that provides exploratorydirection is presented as a controller. The proposed method improves thetraditional exploration scheme by changing exploration dynamically. We thenpresent a novel algorithm exploiting the proposed directional controller forboth policy and critic modification. The presented algorithm outperforms modernreinforcement learning algorithms across a variety of problems from DMControlsuite.

Guided Exploration in Reinforcement Learning via Monte Carlo Critic Optimization