Interpretable Imitation Learning with Dynamic Causal Relations. (arXiv:2310.00489v4 [cs.LG] UPDATED)

Imitation learning, which learns agent policy by mimicking expert
demonstration, has shown promising results in many applications such as medical
treatment regimes and self-driving vehicles. However, it remains a difficult
task to interpret control policies learned by the agent. Difficulties mainly
come from two aspects: 1) agents in imitation learning are usually implemented
as deep neural networks, which are black-box models and lack interpretability;
2) the latent causal mechanism behind agents' decisions may vary along the
trajectory, rather than staying …

agent agents applications arxiv control cs.lg driving dynamic expert imitation learning medical policy relations self-driving self-driving vehicles treatment vehicles