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Interaction-Grounded Learning with Action-inclusive Feedback
Tengyang Xie · Akanksha Saran · Dylan Foster · Lekan Molu · Ida Momennejad · Nan Jiang · Paul Mineiro · John Langford

Consider the problem setting of Interaction-Grounded Learning (IGL), in which a learner's goal is to optimally interact with the environment with no explicit reward to ground its policies. The agent observes a context vector, takes an action, and receives a feedback vector, using this information to effectively optimize a policy with respect to a latent reward function. Prior analyzed approaches fail when the feedback vector contains the action, which significantly limits IGL’s success in many potential scenarios such as Brain-computer interface (BCI) or Human-computer interface (HCI) applications. We address this by creating an algorithm and analysis which allows IGL to work even when the feedback vector contains the action, encoded in any fashion. We provide theoretical guarantees and large-scale experiments based on supervised datasets to demonstrate the effectiveness of the new approach.

Author Information

Tengyang Xie (University of Illinois at Urbana-Champaign)
Akanksha Saran (Microsoft Research)
Dylan Foster (Microsoft Research)
Lekan Molu (Microsoft Research)
Ida Momennejad (Microsoft Research)
Nan Jiang (University of Illinois at Urbana-Champaign)
Paul Mineiro (Microsoft)
John Langford (Microsoft Research)

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