  When learning common skills like driving, beginners usually have domain
experts standing by to ensure the safety of the learning process. We formulate
such learning scheme under the Expert-in-the-loop Reinforcement Learning where
a guardian is introduced to safeguard the exploration of the learning agent.
While allowing the sufficient exploration in the uncertain environment, the
guardian intervenes under dangerous situations and demonstrates the correct
actions to avoid potential accidents. Thus ERL enables both exploration and
expert's partial demonstration as two training sources. Following such a
setting, we develop a novel Expert Guided Policy Optimization (EGPO) method
which integrates the guardian in the loop of reinforcement learning. The
guardian is composed of an expert policy to generate demonstration and a switch
function to decide when to intervene. Particularly, a constrained optimization
technique is used to tackle the trivial solution that the agent deliberately
behaves dangerously to deceive the expert into taking over. Offline RL
technique is further used to learn from the partial demonstration generated by
the expert. Safe driving experiments show that our method achieves superior
training and test-time safety, outperforms baselines with a substantial margin
in sample efficiency, and preserves the generalizabiliy to unseen environments
in test-time. Demo video and source code are available at:
https://decisionforce.github.io/EGPO/
