Learn to Teach: Improve Sample Efficiency in Teacher-student Learning
  for Sim-to-Real Transfer
Feiyang Wu Zhaoyuan Gu Ye Zhao Anqi Wu
Simulation-to-reality (sim-to-real) transfer is a fundamental problem for
robot learning. Domain Randomization, which adds randomization during training,
is a powerful technique that effectively addresses the sim-to-real gap.
However, the noise in observations makes learning significantly harder.
Recently, studies have shown that employing a teacher-student learning paradigm
can accelerate training in randomized environments. Learned with privileged
information, a teacher agent can instruct the student agent to operate in noisy
environments. However, this approach is often not sample efficient as the
experience collected by the teacher is discarded completely when training the
student, wasting information revealed by the environment. In this work, we
extend the teacher-student learning paradigm by proposing a sample efficient
learning framework termed Learn to Teach (L2T) that recycles experience
collected by the teacher agent. We observe that the dynamics of the
environments for both agents remain unchanged, and the state space of the
teacher is coupled with the observation space of the student. We show that a
single-loop algorithm can train both the teacher and student agents under both
Reinforcement Learning and Inverse Reinforcement Learning contexts. We
implement variants of our methods, conduct experiments on the MuJoCo benchmark,
and apply our methods to the Cassie robot locomotion problem. Extensive
experiments show that our method achieves competitive performance while only
requiring environmental interaction with the teacher.