Guided Exploration in Reinforcement Learning via Monte Carlo Critic Optimization
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The class of deep deterministic off-policy algorithms is effectively applied to solve challenging continuous control problems. However, current approaches use 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 of exploratory calibration during the training process. We address these challenges by proposing a novel guided exploration method that uses a differential directional controller to incorporate scalable exploratory action correction. An ensemble of Monte Carlo Critics that provides exploratory direction is presented as a controller. The proposed method improves the traditional exploration scheme by changing exploration dynamically. We then present a novel algorithm exploiting the proposed directional controller for both policy and critic modification. The presented algorithm outperforms modern reinforcement learning algorithms across a variety of problems from DMControl suite.
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