159 citations · 492 across the 75 of their papers we have counts for
16 papers · 1 filter
State-wise Constrained Policy Optimization
Weiye Zhao, Rui Chen, Yifan Sun +2
Reinforcement Learning (RL) algorithms have shown tremendous success in simulation environments, but their application to real-world problems faces significant challenges, with saf…
GUARD: A Safe Reinforcement Learning Benchmark
Weiye Zhao, Yifan Sun, Feihan Li +4
Due to the trial-and-error nature, it is typically challenging to apply RL algorithms to safety-critical real-world applications, such as autonomous driving, human-robot interactio…
Safe and Sample-efficient Reinforcement Learning for Clustered Dynamic Environments
Hongyi Chen, Changliu Liu
This study proposes a safe and sample-efficient reinforcement learning (RL) framework to address two major challenges in developing applicable RL algorithms: satisfying safety cons…
State-wise Safe Reinforcement Learning: A Survey
Weiye Zhao, Tairan He, Rui Chen +2
Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation environments, applying RL to real-world applications still faces many challenges. A major con…
AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning
Tairan He, Weiye Zhao, Changliu Liu
Safety is a critical hurdle that limits the application of deep reinforcement learning (RL) to real-world control tasks. To this end, constrained reinforcement learning leverages c…
First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)
Christopher Brix, Mark Niklas Müller, Stanley Bak +2
This paper presents a summary and meta-analysis of the first three iterations of the annual International Verification of Neural Networks Competition (VNN-COMP) held in 2020, 2021,…
ARC - Actor Residual Critic for Adversarial Imitation Learning
Ankur Deka, Changliu Liu, Katia Sycara
Adversarial Imitation Learning (AIL) is a class of popular state-of-the-art Imitation Learning algorithms commonly used in robotics. In AIL, an artificial adversary's misclassifica…
An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms
Abulikemu Abuduweili, Changliu Liu
Deep reinforcement learning has the potential to address various scientific problems. In this paper, we implement an optics simulation environment for reinforcement learning based…
Online Adaptation of Neural Network Models by Modified Extended Kalman Filter for Customizable and Transferable Driving Behavior Prediction
Letian Wang, Yeping Hu, Changliu Liu
High fidelity behavior prediction of human drivers is crucial for efficient and safe deployment of autonomous vehicles, which is challenging due to the stochasticity, heterogeneity…
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning
Haitong Ma, Changliu Liu, Shengbo Eben Li +3
In the trial-and-error mechanism of reinforcement learning (RL), a notorious contradiction arises when we expect to learn a safe policy: how to learn a safe policy without enough d…
Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning
Haitong Ma, Changliu Liu, Shengbo Eben Li +2
Safety is the major consideration in controlling complex dynamical systems using reinforcement learning (RL), where the safety certificate can provide provable safety guarantee. A…
A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning
Zhenggang Tang, Kai Yan, Liting Sun +2
Microscopic epidemic models are powerful tools for government policy makers to predict and simulate epidemic outbreaks, which can capture the impact of individual behaviors on the…
Data Efficient Human Intention Prediction: Leveraging Neural Network Verification and Expert Guidance
Ruixuan Liu, Changliu Liu
Predicting human intention is critical to facilitating safe and efficient human-robot collaboration (HRC). However, it is challenging to build data-driven models for human intentio…
Online Verification of Deep Neural Networks under Domain Shift or Network Updates
Tianhao Wei, Changliu Liu
Although neural networks are widely used, it remains challenging to formally verify the safety and robustness of neural networks in real-world applications. Existing methods are de…
Robust Online Model Adaptation by Extended Kalman Filter with Exponential Moving Average and Dynamic Multi-Epoch Strategy
Abulikemu Abuduweili, Changliu Liu
High fidelity behavior prediction of intelligent agents is critical in many applications. However, the prediction model trained on the training set may not generalize to the testin…
Algorithms for Verifying Deep Neural Networks
Changliu Liu, Tomer Arnon, Christopher Lazarus +3
Deep neural networks are widely used for nonlinear function approximation with applications ranging from computer vision to control. Although these networks involve the composition…