Hierarchical Reward Design from Language: Enhancing Alignment of Agent Behavior with Human Specifications

Researchers propose Hierarchical Reward Design from Language (HRDL), a new framework for aligning AI agent behavior with human specifications. The method converts natural language instructions into reward functions for reinforcement learning, enabling nuanced behavioral control in complex tasks. Language to Hierarchical Rewards (L2HR) solution captures detailed human preferences beyond simple task completion, improving AI alignment with human expectations in long-horizon tasks.