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The Teacher-Student Framework (TSF) is a reinforcement learning setting wherea teacher agent guards the training of a student agent by intervening andproviding online demonstrations. Assuming optimal, the teacher policy has theperfect timing and capability to intervene in the learning process of thestudent agent, providing safety guarantee and exploration guidance.Nevertheless, in many real-world settings it is expensive or even impossible toobtain a well-performing teacher policy. In this work, we relax the assumptionof a well-performing teacher and develop a new method that can incorporatearbitrary teacher policies with modest or inferior performance. We instantiatean Off-Policy Reinforcement Learning algorithm, termed Teacher-Student SharedControl (TS2C), which incorporates teacher intervention based ontrajectory-based value estimation. Theoretical analysis validates that theproposed TS2C algorithm attains efficient exploration and substantial safetyguarantee without being affected by the teacher's own performance. Experimentson various continuous control tasks show that our method can exploit teacherpolicies at different performance levels while maintaining a low training cost.Moreover, the student policy surpasses the imperfect teacher policy in terms ofhigher accumulated reward in held-out testing environments. Code is availableat https://metadriverse.github.io/TS2C.

Guarded Policy Optimization with Imperfect Online Demonstrations