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Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration

Safe Reinforcement Learning (RL) aims to find a policy that achieves high rewards while satisfying cost constraints. When learning from …

Jinning Li, Xinyi Liu, Banghua Zhu, Jiantao Jiao, Masayoshi Tomizuka, Chen Tang, Wei Zhan

Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning

Offline Reinforcement learning (RL) has shown potent in many safe-critical tasks in robotics where exploration is risky and expensive. …

Jinning Li, Chen Tang, Masayoshi Tomizuka, Wei Zhan

Dealing with the Unknown: Pessimistic Offline Reinforcement Learning

Reinforcement Learning (RL) has been shown effective in domains where the agent can learn policies by actively interacting with its …

Jinning Li, Chen Tang, Masayoshi Tomizuka, Wei Zhan

Dealing with the Unknown: Pessimistic Offline Reinforcement Learning

A safe hierarchical planning framework for complex driving scenarios based on reinforcement learning

Autonomous vehicles need to handle various traffic conditions and make safe and efficient decisions and maneuvers. However, on the one …

Jinning Li, Liting Sun, Jianyu Chen, Masayoshi Tomizuka, Wei Zhan

Interaction-aware behavior planning for autonomous vehicles validated with real traffic data

Autonomous vehicles (AVs) need to interact with other traffic participants who can be either cooperative or aggressive, attentive or …

Jinning Li, Liting Sun, Wei Zhan, Masayoshi Tomizuka

A Novel Integrated SVM for Fault Diagnosis Using KPCA and GA

Fault diagnosis has been more and more significant in modern factories to ensure the proper functionality of the manufacturing process …

Jinning Li

A Novel Integrated SVM for Fault Diagnosis Using KPCA and GA