This week we have our very own Dzmitry Bahadanau giving a talk on work he did on internship at DeepMind on Friday June 22 at 10:30AM in room AA3195.

Help Dima get a human baseline for instruction following and come to the tea talk
Michael

TITLE Learning to Follow Language Instructions with Adversarial Reward Induction

KEYWORDS language understanding, reinforcement learning, grounded language

ABSTRACT

Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, for many real-world natural language commands that involve a degree of underspecification or ambiguity, such as tidy the room, it would be challenging or impossible to program an appropriate reward function. To overcome this, we present a method for learning to follow commands from a training set of instructions and corresponding example goal-states, rather than an explicit reward function. Importantly, the example goal-states are not seen at test time. The approach effectively separates the representation of what instructions require from how they can be executed. In a simple grid world, the method enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements. We further show the method allows our agent to adapt to unseen instructions and changes in the environment without requiring new training examples.