BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay

arXiv:2402.14194v1 Announce Type: new
Abstract: Imitation learning learns a policy from demonstrations without requiring hand-designed reward functions. In many robotic tasks, such as autonomous racing, imitated policies must model complex environment dynamics and human decision-making. Sequence modeling is highly effective in capturing intricate patterns of motion sequences but struggles to adapt to new environments or distribution shifts that are common in real-world robotics tasks. In contrast, Adversarial Imitation Learning (AIL) can mitigate this effect, but struggles with sample inefficiency and …

adversarial arxiv behavior cs.lg cs.ro human imitation learning racing transformer type