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09/13/2023
Safe Reinforcement Learning with Dual Robustness
Reinforcement learning (RL) agents are vulnerable to adversarial disturb...
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04/18/2023
Feasible Policy Iteration
Safe reinforcement learning (RL) aims to solve an optimal control proble...
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12/03/2022
Smoothing Policy Iteration for Zero-sum Markov Games
Zero-sum Markov Games (MGs) has been an efficient framework for multi-ag...
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10/19/2022
Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification
Self-evolution is indispensable to realize full autonomous driving. This...
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10/14/2022
Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate
Safe reinforcement learning (RL) that solves constraint-satisfactory pol...
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09/23/2022
Synthesize Efficient Safety Certificates for Learning-Based Safe Control using Magnitude Regularization
Energy-function-based safety certificates can provide provable safety gu...
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09/11/2022
Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning
Choosing an appropriate parameter set for the designed controller is cri...
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05/16/2022
Reachability Constrained Reinforcement Learning
Constrained reinforcement learning (CRL) has gained significant interest...
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04/09/2022
Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learning
Intersections are quite challenging among various driving scenes wherein...
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01/29/2022
Zeroth-Order Actor-Critic
Zeroth-order optimization methods and policy gradient based first-order ...
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11/25/2021
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning
In the trial-and-error mechanism of reinforcement learning (RL), a notor...
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11/15/2021
Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning
Safety is the major consideration in controlling complex dynamical syste...
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10/24/2021
Encoding Integrated Decision and Control for Autonomous Driving with Mixed Traffic Flow
Reinforcement learning (RL) has been widely adopted to make intelligent ...
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09/12/2021
Encoding Distributional Soft Actor-Critic for Autonomous Driving in Multi-lane Scenarios
In this paper, we propose a new reinforcement learning (RL) algorithm, c...
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08/30/2021
Integrated Decision and Control at Multi-Lane Intersections with Mixed Traffic Flow
Autonomous driving at intersections is one of the most complicated and a...
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08/26/2021
Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian
Safety is essential for reinforcement learning (RL) applied in the real ...
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05/24/2021
Fixed-Dimensional and Permutation Invariant State Representation of Autonomous Driving
In this paper, we propose a new state representation method, called enco...
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03/18/2021
Integrated Decision and Control: Towards Interpretable and Efficient Driving Intelligence
Decision and control are two of the core functionalities of high-level a...
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03/09/2021
Approximate Optimal Filter for Linear Gaussian Time-invariant Systems
State estimation is critical to control systems, especially when the sta...
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03/08/2021
Decision-Making under On-Ramp merge Scenarios by Distributional Soft Actor-Critic Algorithm
Merging into the highway from the on-ramp is an essential scenario for a...
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03/02/2021
Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function
Model information can be used to predict future trajectories, so it has ...
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02/23/2021
Recurrent Model Predictive Control
This paper proposes an off-line algorithm, called Recurrent Model Predic...
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02/23/2021
Mixed Policy Gradient
Reinforcement learning (RL) has great potential in sequential decision-m...
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02/17/2021
Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning
Safety is essential for reinforcement learning (RL) applied in real-worl...
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02/16/2021
Steadily Learn to Drive with Virtual Memory
Reinforcement learning has shown great potential in developing high-leve...
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12/19/2020
Model-Based Actor-Critic with Chance Constraint for Stochastic System
Safety constraints are essential for reinforcement learning (RL) applied...
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07/14/2020
Ternary Policy Iteration Algorithm for Nonlinear Robust Control
The uncertainties in plant dynamics remain a challenge for nonlinear con...
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03/03/2020
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization
Reinforcement learning (RL) is attracting increasing interests in autono...
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02/28/2020
Mixed Reinforcement Learning with Additive Stochastic Uncertainty
Reinforcement learning (RL) methods often rely on massive exploration da...
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02/13/2020
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic
Reinforcement learning (RL) has achieved remarkable performance in a var...
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01/23/2020
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
Unlike popular modularized framework, end-to-end autonomous driving seek...
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01/09/2020
Addressing Value Estimation Errors in Reinforcement Learning with a State-Action Return Distribution Function
In current reinforcement learning (RL) methods, function approximation e...
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12/23/2019
Direct and indirect reinforcement learning
Reinforcement learning (RL) algorithms have been successfully applied to...
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12/18/2019
Centralized Conflict-free Cooperation for Connected and Automated Vehicles at Intersections by Proximal Policy Optimization
Connected vehicles will change the modes of future transportation manage...
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11/26/2019
Deep adaptive dynamic programming for nonaffine nonlinear optimal control problem with state constraints
This paper presents a constrained deep adaptive dynamic programming (CDA...
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09/11/2019
Generalized Policy Iteration for Optimal Control in Continuous Time
This paper proposes the Deep Generalized Policy Iteration (DGPI) algorit...
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06/06/2019