Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion

There is a growing interest in developing automated agents that can work alongside humans. In addition to completing the assigned task, such an agent will undoubtedly be expected to behave in a manner that is preferred by the human. This requires the human to communicate their preferences to the agent. To achieve this, the current approaches either require the users to specify the reward function or the preference is interactively learned from queries that ask the user to compare trajectories. The former approach can be challenging if the internal representation used by the agent is inscrutable to the human while the latter is unnecessarily cumbersome for the user if their preference can be specified more easily in symbolic terms. In this work, we propose PRESCA (PREference Specification through Concept Acquisition), a system that allows users to specify their preferences in terms of concepts that they understand. PRESCA maintains a set of such concepts in a shared vocabulary. If the relevant concept is not in the shared vocabulary, then it is learned. To make learning a new concept more efficient, PRESCA leverages causal associations between the target concept and concepts that are already known. Additionally, the effort of learning the new concept is amortized by adding the concept to the shared vocabulary for supporting preference specification in future interactions. We evaluate PRESCA by using it on a Minecraft environment and show that it can be effectively used to make the agent align with the user's preference.