Safe Reinforcement Learning with Contrastive Risk Prediction

Hanping Zhang, Yuhong Guo

Keywords: safe reinforcement learning, contrastive risk prediction

TL;DR: We propose a contrastive risk prediction method to train safe RL agents with risk preventive trajectory exploration and reward shaping.

Abstract: As safety violations can lead to severe consequences in real-world applications, the increasing deployment of Reinforcement Learning (RL) in safety-critical domains such as robotics has propelled the study of safe exploration for reinforcement learning (safe RL). In this work, we propose a risk preventive training method for safe RL, which learns a statistical contrastive classifier to predict the probability of a state-action pair leading to unsafe states. Based on the predicted risk probabilities, we can collect risk preventive trajectories and reshape the reward function with risk penalties to induce safe RL policies. We conduct experiments in robotic simulation environments. The results show the proposed approach has comparable performance with the state-of-the-art model-based methods and outperforms conventional model-free safe RL approaches.

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