Learning to Influence Human Behavior with Offline Reinforcement Learning

In the real world, some of the most complex settings for learned agents involve interaction with humans, who often exhibit suboptimal, unpredictable behavior due to sophisticated biases. Agents that interact with people in such settings end up influencing the actions that these people take. Our goal in this work is to enable agents to leverage that influence to improve the human's performance in collaborative tasks, as the task unfolds. Unlike prior work, we do not assume online training with people (which tends to be too expensive and unsafe), nor access to a high fidelity simulator of the environment. Our idea is that by taking a variety of previously observed human-human interaction data and labeling it with the task reward, offline reinforcement learning (RL) can learn to combine components of behavior, and uncover actions that lead to more desirable human actions. First, we show that offline RL can learn strategies to influence and improve human behavior, despite those strategies not appearing in the dataset, by utilizing components of diverse, suboptimal interactions. In addition, we demonstrate that offline RL can learn influence that adapts with humans, thus achieving long-term coordination with them even when their behavior changes. We evaluate our proposed method with real people in the Overcooked collaborative benchmark domain, and demonstrate successful improvement in human performance.