Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies
This addresses the challenge of safely leveraging imperfect baseline policies in reinforcement learning for applications like robotics or autonomous systems, though it is incremental as it builds on existing safe RL methods.
The paper tackles the problem of safe reinforcement learning with a baseline policy that may not satisfy constraints, proposing an iterative algorithm that alternates between maximizing reward, minimizing distance to the baseline, and projecting onto constraint-satisfying sets. It achieves 10 times fewer constraint violations and 40% higher reward on average in experiments on five control tasks.
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.