Imitation Based

Imitation learning aims to train agents by mimicking expert demonstrations, offering a powerful alternative to reinforcement learning, particularly when reward design is challenging. Current research focuses on improving sample efficiency, addressing limitations like covariate shift in behavioral cloning and the high sample complexity of adversarial imitation learning, often through planning-based approaches and novel architectures like those incorporating attention mechanisms. These advancements are driving progress in diverse applications, including autonomous driving and multi-agent interaction, by enabling the creation of more robust and data-efficient intelligent systems.