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Reinforcement learning algorithms are known to be sample inefficient, andoften performance on one task can be substantially improved by leveraginginformation (e.g., via pre-training) on other related tasks. In this work, wepropose a technique to achieve such knowledge transfer in cases where agenttrajectories contain sensitive or private information, such as in thehealthcare domain. Our approach leverages a differentially private policyevaluation algorithm to initialize an actor-critic model and improve theeffectiveness of learning in downstream tasks. We empirically show thistechnique increases sample efficiency in resource-constrained control problemswhile preserving the privacy of trajectories collected in an upstream task.

Actor Critic with Differentially Private Critic