We tackle real-world problems with complex structures beyond the pixel-based
game or simulator. We formulate it as a few-shot reinforcement learning problem
where a task is characterized by a subtask graph that defines a set of subtasks
and their dependencies that are unknown to the agent. Different from the
previous meta-rl methods trying to directly infer the unstructured task
embedding, our multi-task subtask graph inferencer (MTSGI) first infers the
common high-level task structure in terms of the subtask graph from the
training tasks, and use it as a prior to improve the task inference in testing.
Our experiment results on 2D grid-world and complex web navigation domains show
that the proposed method can learn and leverage the common underlying structure
of the tasks for faster adaptation to the unseen tasks than various existing
algorithms such as meta reinforcement learning, hierarchical reinforcement
learning, and other heuristic agents.

本研究提出基于多任务子任务图推断的少样本强化学习方法，通过推断训练任务的高层子任务图结构，提高在测试任务中的任务推断和适应能力，实验结果表明其能够学习并利用任务的潜在共同结构，比其他现有算法如元强化学习、分层强化学习和启发式智能体等更快地适应未见过的任务。