research
∙
12/03/2022
Smoothing Policy Iteration for Zero-sum Markov Games
Zero-sum Markov Games (MGs) has been an efficient framework for multi-ag...
share
research
∙
10/14/2022
Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate
Safe reinforcement learning (RL) that solves constraint-satisfactory pol...
research
∙
09/12/2022
On the Optimization Landscape of Dynamic Output Feedback: A Case Study for Linear Quadratic Regulator
The convergence of policy gradient algorithms in reinforcement learning ...
research
∙
08/18/2022
Global Convergence of Two-timescale Actor-Critic for Solving Linear Quadratic Regulator
The actor-critic (AC) reinforcement learning algorithms have been the po...
research
∙
04/09/2022
Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learning
Intersections are quite challenging among various driving scenes wherein...
research
∙
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 ...
research
∙
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...
research
∙
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 ...
research
∙
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...
research
∙
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...
research
∙
03/09/2021
Approximate Optimal Filter for Linear Gaussian Time-invariant Systems
State estimation is critical to control systems, especially when the sta...
research
∙
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...
research
∙
02/23/2021
Recurrent Model Predictive Control
This paper proposes an off-line algorithm, called Recurrent Model Predic...
research
∙
02/23/2021
Mixed Policy Gradient
Reinforcement learning (RL) has great potential in sequential decision-m...
research
∙
02/17/2021
Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning
Safety is essential for reinforcement learning (RL) applied in real-worl...
research
∙
07/14/2020
Ternary Policy Iteration Algorithm for Nonlinear Robust Control
The uncertainties in plant dynamics remain a challenge for nonlinear con...
research
∙
03/03/2020
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization
Reinforcement learning (RL) is attracting increasing interests in autono...
research
∙
02/13/2020
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic
Reinforcement learning (RL) has achieved remarkable performance in a var...
research
∙
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...
research
∙
12/23/2019
Direct and indirect reinforcement learning
Reinforcement learning (RL) algorithms have been successfully applied to...
research
∙
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...
research
∙
09/11/2019