51 citations · 51 across the 11 of their papers we have counts for
12 papers
Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving
Dechen Xie, Mushuang Liu
This paper presents a comprehensive comparison of four representative reinforcement learning (RL) methods in autonomous vehicle (AV) decision-making, including: Fitted Value Iterat…
Hardware-in-the-Loop Evaluation of Game-Theoretic Autonomous Driving
Aakanksha Kataria, Huiwen Yan, Mushuang Liu
This paper evaluates Nash- and Stackelberg-based decision-making controllers for autonomous intersection crossing using a three-stage evaluation pipeline culminating in physical Qu…
Real-Time Solution-Seeking for Game-Theoretic Autonomous Driving via Time-Distributed Iterations
Shaoqing Liu, Mushuang Liu
Computational complexity has been a major challenge in game-theoretic model predictive control (GT-MPC), as real-time solutions to a game (e.g., Nash equilibria (NEs)) have to be c…
Markov Potential Game and Multi-Agent Reinforcement Learning for Autonomous Driving
Huiwen Yan, Mushuang Liu
Autonomous driving (AD) requires safe and reliable decision-making among interacting agents, e.g., vehicles, bicycles, and pedestrians. Multi-agent reinforcement learning (MARL) mo…
Directed-MAML: Meta Reinforcement Learning Algorithm with Task-directed Approximation
Yang Zhang, Huiwen Yan, Mushuang Liu
Model-Agnostic Meta-Learning (MAML) is a versatile meta-learning framework applicable to both supervised learning and reinforcement learning (RL). However, applying MAML to meta-re…
Hierarchical Game-Based Multi-Agent Decision-Making for Autonomous Vehicles
Mushuang Liu, Yan Wan, Frank Lewis +3
This paper develops a game-theoretic decision-making framework for autonomous driving in multi-agent scenarios. A novel hierarchical game-based decision framework is developed for…
Markov Potential Game Construction and Multi-Agent Reinforcement Learning with Applications to Autonomous Driving
Huiwen Yan, Mushuang Liu
Markov games (MGs) provide a mathematical foundation for multi-agent reinforcement learning (MARL), enabling self-interested agents to learn their optimal policies while interactin…
Newton Methods in Generalized Nash Equilibrium Problems with Applications to Game-Theoretic Model Predictive Control
Mushuang Liu, Ilya Kolmanovsky
We prove input-to-state stability (ISS) of perturbed Newton-type methods for generalized equations arising from Nash equilibrium (NE) and generalized NE (GNE) problems. This ISS pr…
Game Projection and Robustness for Game-Theoretic Autonomous Driving
Mushuang Liu, H. Eric Tseng, Dimitar Filev +2
Game-theoretic approaches are envisioned to bring human-like reasoning skills and decision-making processes for autonomous vehicles (AVs). However, challenges including game comple…
Nash or Stackelberg? -- A comparative study for game-theoretic AV decision-making
Brady Bateman, Ming Xin, H. Eric Tseng +1
This paper studies game-theoretic decision-making for autonomous vehicles (AVs). A receding horizon multi-player game is formulated to model the AV decision-making problem. Two cla…
Safe and Human-Like Autonomous Driving: A Predictor-Corrector Potential Game Approach
Mushuang Liu, H. Eric Tseng, Dimitar Filev +2
This paper proposes a novel decision-making framework for autonomous vehicles (AVs), called predictor-corrector potential game (PCPG), composed of a Predictor and a Corrector. To e…
Potential Game-Based Decision-Making for Autonomous Driving
Mushuang Liu, Ilya Kolmanovsky, H. Eric Tseng +3
Decision-making for autonomous driving is challenging, considering the complex interactions among multiple traffic agents (e.g., autonomous vehicles (AVs), human drivers, and pedes…