  Properly defining a reward signal to efficiently train a reinforcement
learning (RL) agent is a challenging task. Designing balanced objective
functions from which a desired behavior can emerge requires expert knowledge,
especially for complex environments. Learning rewards from human feedback or
using large language models (LLMs) to directly provide rewards are promising
alternatives, allowing non-experts to specify goals for the agent. However,
black-box reward models make it difficult to debug the reward. In this work, we
propose Object-Centric Assessment with Language Models (OCALM) to derive
inherently interpretable reward functions for RL agents from natural language
task descriptions. OCALM uses the extensive world-knowledge of LLMs while
leveraging the object-centric nature common to many environments to derive
reward functions focused on relational concepts, providing RL agents with the
ability to derive policies from task descriptions.
