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Beyond Rewards : Learning from Richer Supervision

Recently there has been some interest in the reinforcement learning community on learning from richer feedback from the environment rather than just a scalar reward signal. In this paper we look at the question of learning from sporadic instructions from a human. Instructions can take several forms, from complete specification of policies, to directing the agent’s search to specific parts of the state space. We propose a framework that allows the instructor to specify which objects to attend to while solving a task and report some simple validation experiments.