Interaction-Grounded Learning with Action-inclusive Feedback. (arXiv:2206.08364v1 [cs.LG])

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

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