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Found 2 papers, 1 papers with code
Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization
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.
Hierarchical Reinforcement Learning
Meta Reinforcement Learning
+2
Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies
We propose and address a novel few-shot RL problem, where a task is characterized by a subtask graph which describes a set of subtasks and their dependencies that are unknown to the agent.
