As reinforcement learning techniques are increasingly applied to real-world decision problems, attention has turned to how these algorithms use potentially sensitive information.We consider the task of training a policy that maximizes reward while minimizing disclosure of certain sensitive state variables through the actions.We give examples of how this setting covers real-world problems in privacy for sequential decision-making.We solve this problem in the policy gradients framework by introducing a regularizer based on the mutual information (MI) between the sensitive state and the actions.We develop a model-based stochastic gradient estimator for optimization of privacy-constrained policies. We also discuss an alternative MI regularizer that serves as an upper bound to our main MI regularizer and can be optimized in a model-free setting, and a powerful direct estimator that can be used in an environment with differentiable dynamics.We contrast previous work in differentially-private RL to our mutual-information formulation of information disclosure.Experimental results show that our training method results in policies that hide the sensitive state, even in challenging high-dimensional tasks.