Hierarchical Reinforcement Learning

PyCRM: A Python library for reward machine-based reinforcement learning
Reinforcement Learning (RL) research often models environments as Markov decision processes. Yet many real-world tasks are …
Tristan Bester, Geraud Nangue Tasse, Benjamin Rosman, Steven James
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Drowning in Degrees of Freedom: One Agent for Every Task
We describe a research programme aimed at the construction of a single, generally intelligent agent—one competent across all …
Steven James
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Unsupervised Hierarchical Skill Discovery
We consider the problem of unsupervised skill segmentation and hierarchical structure discovery in reinforcement learning. While recent …
Damion Harvey, Geraud Nangue Tasse, Benjamin Rosman, Branden Ingram, Steven James
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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 …
Alper Ahmetoglu, Steven James, Cameron Allen, Sam Lobel, David Abel, George Konidaris
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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 …
Tristan Bester, Benjamin Rosman, Steven James, Geraud Nangue Tasse
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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 …
Matthew Cockcroft, Shahil Mawjee, Steven James, Pravesh Ranchod
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