Hierarchical Reinforcement Learning

Unsupervised Hierarchical Skill Discovery
We consider the problem of unsupervised skill segmentation and hierarchical structure discovery in reinforcement learning. While recent …
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Skill-Driven Neurosymbolic State Abstractions
We consider how to construct state abstractions compatible with a given set of abstract actions, to obtain a well-formed abstract …
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Counting Reward Automata: Sample Efficient Reinforcement Learning Through the Exploitation of Reward Function Structure
We present counting reward automata—a finite state machine variant capable of modelling any reward function expressible as a …
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Hierarchical Reinforcement Learning with AI Planning Models
Deep Reinforcement Learning (DRL) has shown breakthroughs in solving challenging problems, such as pixel-based games and continuous …
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Learning Options from Demonstration using Skill Segmentation
We present a method for learning options from segmented demonstration trajectories. The trajectories are first segmented into skills …
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Hierarchy Through Composition with Multitask LMDPs
Hierarchical architectures are critical to the scalability of reinforcement learning methods. Most current hierarchical frameworks …
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