6 papers
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…
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…
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…
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…