Learning and Repair of Deep Reinforcement Learning Policies from Fuzz-Testing Data

Reinforcement learning from demonstrations (RLfD) is a promising approach to improve the exploration efficiency of reinforcement learning (RL) by learning from expert demonstrations in addition to interactions with the environment. In this paper, we propose a framework that combines techniques from search-based testing with RLfD with the goal to raise the level of dependability of RL policies and to reduce human engineering effort. Within our framework, we provide methods for efficiently training, evaluating, and repairing RL policies. Instead of relying on the costly collection of demonstrations from (human) experts, we automatically compute a diverse set of demonstrations via search-based fuzzing methods and use the fuzz demonstrations for RLfD. To evaluate the safety and robustness of the trained RL agent, we search for safety-critical scenarios in the black-box environment. Finally, when unsafe behavior is detected, we compute demonstrations through fuzz testing that represent safe behavior and use them to repair the policy. Our experiments show that our framework is able to efficiently learn high-performing and safe policies without requiring any expert knowledge.