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An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch
Siddharth Desai · Ishan Durugkar · Haresh Karnan · Garrett Warnell · Josiah Hanna · Peter Stone

Mon Dec 07 09:00 PM -- 11:00 PM (PST) @ Poster Session 0 #48

We examine the problem of transferring a policy learned in a source environment to a target environment with different dynamics, particularly in the case where it is critical to reduce the amount of interaction with the target environment during learning. This problem is particularly important in sim-to-real transfer because simulators inevitably model real-world dynamics imperfectly. In this paper, we show that one existing solution to this transfer problem-- grounded action transformation --is closely related to the problem of imitation from observation (IfO): learning behaviors that mimic the observations of behavior demonstrations. After establishing this relationship, we hypothesize that recent state-of-the-art approaches from the IfO literature can be effectively repurposed for grounded transfer learning. To validate our hypothesis we derive a new algorithm -- generative adversarial reinforced action transformation (GARAT) -- based on adversarial imitation from observation techniques. We run experiments in several domains with mismatched dynamics, and find that agents trained with GARAT achieve higher returns in the target environment compared to existing black-box transfer methods.

Author Information

Siddharth Desai (University of Texas at Austin)
Ishan Durugkar (University of Texas at Austin)
Haresh Karnan (University of Texas at Austin)
Garrett Warnell (US Army Research Laboratory)
Josiah Hanna (University of Edinburgh)
Peter Stone (The University of Texas at Austin, Sony AI)

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