Uncertainty-Aware Model-Based Reinforcement Learning: Methodology and Application in Autonomous Driving
Jingda Wu, Zhiyu Huang, Chen Lv
Abstract
To further improve learning efficiency and performance of reinforcement learning (RL), a novel uncertainty-aware model-based RL method is proposed and validated in autonomous driving scenarios in this paper. First, an action-conditioned ensemble model with the capability of uncertainty assessment is established as the environment model. Then, a novel uncertainty-aware model-based RL method is developed based on the adaptive truncation approach, providing virtual interactions between the agent and environment model, and improving RL’s learning efficiency and performance. The proposed method is then implemented in end-to-end autonomous vehicle control tasks, validated and compared with state-of-the-art methods under various driving scenarios. Validation results suggest that the proposed method outperforms the model-free RL approach with respect to learning efficiency, and model-based approach with respect to both efficiency and performance, demonstrating its feasibility and effectiveness.