IMITATIVE MODELS: PERCEPTION DRIVEN FORE
Imitation learning provides an appealing framework for autonomous control: in many tasks, demonstrations can be readily obtained from human experts, and using demonstration data removes the need for costly and potentially dangerous onpolicy trials in the real world. On the other hand, model based reinforcement learning offers considerably more flexibility: a model learned from data can be reused at test time to achieve a wide variety of goals. In this paper, we aim to combine these benefits to learn imitative models: predictive models that can be used to plan behaviors at test time that achieve user specified goals, while remaining close to the distribution of behaviors seen in the demonstration data. We find that this method substantially improves on the performance of both direct imitation and model based RL in a simulated driving task, and can be learned efficiently and safely using only demonstration data without on policy data collection.