Explore the weakness: Instructive exploration adversarial robust reinforcement learning
Chunyang Wu, Fei Zhu, Quan Liu · Journal of King Saud University - Computer and Information Sciences · 2022
Although reinforcement learning has been proved to be effective in many simulated platforms, it may still fail in environments due to the difference between simulation environment and real world environment, as well as being subjected to unexcepted attacks that objectively exist. Therefore, it calls for improving the robustness of the agent to increase its stability. To address the problem, an algorithm that uses the curiosity mechanism to improve the model exploration, referred to as instructive exploration adversarial robust reinforcement learning(Iearrl), is proposed, which enhances the adaption ability of agents through adversary learning, ensuring that the agent chooses a better action in practical environments with different settings from the training environment. At the same time, in order to increase the efficiency of exploration and reduce the cost, a model used to evaluate the competency of the agent is built for mentoring internal rewards determining whether further exploration is needed by analyzing the agent’s action in the current state space. The experiments in MuJoCo platforms verified the effectiveness of the proposed method.