Learning Communication Policies for Different Follower Behaviors in a Collaborative Reference Game
Keywords: language and vision, reference game, reinforcement learning
TL;DR: We evaluate the adaptability of communicative neural agents to assumed partner behaviors in a collaborative reference game.
Abstract: In this work, we evaluate the adaptability of neural agents towards assumed partner behaviors in a collaborative reference game. In this game, success is achieved when a knowledgeable guide can verbally lead a follower to the selection of a specific puzzle piece among several distractors. We frame this language grounding and coordination task as a reinforcement learning problem and measure to which extent a common reinforcement training algorithm (PPO) is able to produce neural agents (the guides) that perform well with various heuristic follower behaviors that vary along the dimensions of confidence and autonomy. We experiment with a learning signal that in addition to the goal condition also respects an assumed communicative effort. Our results indicate that this novel ingredient leads to communicative strategies that are less verbose (staying silent in some of the steps) and that with respect to that the guide’s strategies indeed adapt to the partner’s level of confidence and autonomy.
Submission Type: Long Paper (8 Pages)
Archival Option: This is an archival submission
Submission Number: 2
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