Web: http://arxiv.org/abs/2206.08364

June 17, 2022, 1:11 a.m. | Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv feedback learning lg

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