4 papers
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…
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…
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…