Multi-Task Reinforcement Learning with Cost-based HTN Planning

Multi-task Reinforcement Learning (MT-RL) faces key challenges in accomplishing complex long-horizon tasks, particularly related to scarce rewards, inefficient sample usage, and low transferability. These challenges are exacerbated in real world scenarios where tasks can often be done by completing different intermediate subtasks, complicating intermediate reward allocation. To address those issues, we introduce a novel framework integrating a strategic planner, a pre-trained language module, and a reinforcement learning policy. This framework strategically decomposes complex tasks into observable sub-task lists using the planner, adapting the plan based on sub-task completion, while the incorporation of the pre-trained language module aids in the task list understanding. We evaluated our framework in a single-agent overcooked environment, chosen for its relevance in the long-horizon tasks. Our results demonstrate notable improvements in time efficiency and adaptability, showcasing the framework's potential to enhance MT-RL applications.