331 citations · 565 across the 13 of their papers we have counts for
7 papers · 1 filter
Smoothing Policy Iteration for Zero-sum Markov Games
Yangang Ren, Yao Lyu, Wenxuan Wang +3
Zero-sum Markov Games (MGs) has been an efficient framework for multi-agent systems and robust control, wherein a minimax problem is constructed to solve the equilibrium policies.…
Self-learned Intelligence for Integrated Decision and Control of Automated Vehicles at Signalized Intersections
Yangang Ren, Jianhua Jiang, Dongjie Yu +4
Intersection is one of the most complex and accident-prone urban scenarios for autonomous driving wherein making safe and computationally efficient decisions is non-trivial. Curren…
Integrated Decision and Control at Multi-Lane Intersections with Mixed Traffic Flow
Jianhua Jiang, Yangang Ren, Yang Guan +3
Autonomous driving at intersections is one of the most complicated and accident-prone traffic scenarios, especially with mixed traffic participants such as vehicles, bicycles and p…
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence
Yang Guan, Yangang Ren, Qi Sun +5
Decision and control are core functionalities of high-level automated vehicles. Current mainstream methods, such as functionality decomposition and end-to-end reinforcement learnin…
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic
Yangang Ren, Jingliang Duan, Shengbo Eben Li +2
Reinforcement learning (RL) has achieved remarkable performance in numerous sequential decision making and control tasks. However, a common problem is that learned nearly optimal p…
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors
Jingliang Duan, Yang Guan, Shengbo Eben Li +2
In reinforcement learning (RL), function approximation errors are known to easily lead to the Q-value overestimations, thus greatly reducing policy performance. This paper presents…
Direct and indirect reinforcement learning
Yang Guan, Shengbo Eben Li, Jingliang Duan +4
Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks. In this paper, we classify RL into dir…