We propose a framework for ensuring safe behavior of a reinforcement learning agent when the reward function may be difficult to specify. In order to do this, we rely on the existence of demonstrations from expert policies, and we provide a theoretical framework for the agent to optimize in the space of rewards consistent with its existing knowledge. We propose two methods to solve the resulting optimization: an exact ellipsoid-based method and a method in the spirit of the follow-the-perturbed-leader algorithm. Our experiments demonstrate the behavior of our algorithm in both discrete and continuous problems. The trained agent safely avoids states with potential negative effects while imitating the behavior of the expert in the other states.

Learning from demonstrations usually faces an ill-posed problem of inferring the expert reward functions. To facilitate safe learning from demonstrations, the paper formulates a maximin learning problem over a convex reward polytope, in order to guarantee that the worst possible consistent reward would yield a policy that is not much worse than optimal. The assumption is that the reward is linear in known features. The authors proposed two method: (i) ellipsoid method and (ii) follow-the-perturbed leader using separation oracles and a given MDP solver. The experiment is done in a grid world setting, and a modified version of the cart-pole problem.

We propose a framework for ensuring safe behavior of a reinforcement learning agent when the reward function may be difficult to specify. In order to do this, we rely on the existence of demonstrations from expert policies, and we provide a theoretical framework for the agent to optimize in the space of rewards consistent with its existing knowledge. We propose two methods to solve the resulting optimization: an exact ellipsoid-based method and a method in the spirit of the "follow-the-perturbed-leader" algorithm. Our experiments demonstrate the behavior of our algorithm in both discrete and continuous problems. The trained agent safely avoids states with potential negative effects while imitating the behavior of the expert in the other states.