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.

提出一种迭代策略优化算法以安全地学习来自基准策略的强化学习问题，其中基准策略可以来自示范数据或教师代理，并且需要满足一组由安全性、公平性或其他特定应用需求编码的约束条件。在控制任务上的实验中，该算法始终优于多个最先进的基线算法，并在平均回报上提高了 40％，约 10 倍少的约束违规行为。