2025
Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments
RA-L 2025
Training intelligent agents to navigate highly interactive environments presents significant challenges. While guided meta reinforcement learning (RL) approach that first trains a guiding policy to train the ego agent has proven effective in improving generalizability across scenarios with various l