An instance-centric representation with local frames, relative positional encodings, and adaptive reward transformation in adversarial IRL yields scalable, accurate, and robust behavior models for multi-agent driving simulation.
Imitation is not enough: Robustifying imitation with reinforcement learning for challenging driving scenarios
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Toward Efficient and Robust Behavior Models for Multi-Agent Driving Simulation
An instance-centric representation with local frames, relative positional encodings, and adaptive reward transformation in adversarial IRL yields scalable, accurate, and robust behavior models for multi-agent driving simulation.
- RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation