Reinforced Natural Language Interfaces via Entropy Decomposition
This addresses the challenge of developing more adaptive and efficient conversational agents for human-AI collaboration, though it appears incremental as it builds on reinforcement learning and entropy decomposition.
The paper tackled the problem of enabling conversational agents to adapt quickly to unseen tasks and learn task-specific communication tactics for complex, temporally extended tasks, achieving effectiveness proven through human and simulation tests on a referential game and a 3D navigation game.
In this paper, we study the technical problem of developing conversational agents that can quickly adapt to unseen tasks, learn task-specific communication tactics, and help listeners finish complex, temporally extended tasks. We find that the uncertainty of language learning can be decomposed to an entropy term and a mutual information term, corresponding to the structural and functional aspect of language, respectively. Combined with reinforcement learning, our method automatically requests human samples for training when adapting to new tasks and learns communication protocols that are succinct and helpful for task completion. Human and simulation test results on a referential game and a 3D navigation game prove the effectiveness of the proposed method.