Qiuhua Huang

Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning

One of the key challenges to deep reinforcement learning (deep RL) is to ensure safety at both training and testing phases. In this work, we propose a novel technique of unsupervised action planning to improve the safety of on-policy reinforcement learning algorithms, such as trust region policy opt…