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Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning
To address this challenge, previous imitation learning methods exploit task-specific knowledge, e. g., labeling demonstrations manually or specifying termination conditions for each sub-task.
Self-Supervised Curriculum Generation for Autonomous Reinforcement Learning without Task-Specific Knowledge
In this paper, we propose a novel ARL algorithm that can generate a curriculum adaptive to the agent's learning progress without task-specific knowledge.
Imagination-Augmented Hierarchical Reinforcement Learning for Safe and Interactive Autonomous Driving in Urban Environments
Modern HRL typically designs a hierarchical agent composed of a high-level policy and low-level policies.
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