  In this work, we establish heuristics and learning strategies for the
autonomous control of a dozer grading an uneven area studded with sand piles.
We formalize the problem as a Markov Decision Process, design a simulation
which demonstrates agent-environment interactions and finally compare our
simulator to a real dozer prototype. We use methods from reinforcement
learning, behavior cloning and contrastive learning to train a hybrid policy.
Our trained agent, AGPNet, reaches human-level performance and outperforms
current state-of-the-art machine learning methods for the autonomous grading
task. In addition, our agent is capable of generalizing from random scenarios
to unseen real world problems.
