  We consider the problem of reinforcement learning when provided with (1) a
baseline control policy and (2) a set of constraints that the learner must
satisfy. The baseline policy can arise from demonstration data or a teacher
agent and may provide useful cues for learning, but it might also be
sub-optimal for the task at hand, and is not guaranteed to satisfy the
specified constraints, which might encode safety, fairness or other
application-specific requirements. In order to safely learn from baseline
policies, we propose an iterative policy optimization algorithm that alternates
between maximizing expected return on the task, minimizing distance to the
baseline policy, and projecting the policy onto the constraint-satisfying set.
We analyze our algorithm theoretically and provide a finite-time convergence
guarantee. In our experiments on five different control tasks, our algorithm
consistently outperforms several state-of-the-art baselines, achieving 10 times
fewer constraint violations and 40% higher reward on average.
