Discovering Hierarchy in Reinforcement Learning
Automatic Modelling of Task-Hierarchies byMachines through Sense-Act Interactions with theirEnvironments
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We are relying more and more on machines to performtasks that were previously the sole domain ofhumans. There is a need to make machines more self-adaptable and for them to set their own sub-goals.Designing machines that can make sense of the worldthey inhabit is still an open research problem.Fortunately many complex environments exhibitstructure that can be modelled as an inter-relatedset of subsystems. Subsystems are often repetitivein time and space and reoccur many times ascomponents of different tasks. A machine may be ableto learn how to tackle larger problems if it cansuccessfully find...
We are relying more and more on machines to performtasks that were previously the sole domain ofhumans. There is a need to make machines more self-adaptable and for them to set their own sub-goals.Designing machines that can make sense of the worldthey inhabit is still an open research problem.Fortunately many complex environments exhibitstructure that can be modelled as an inter-relatedset of subsystems. Subsystems are often repetitivein time and space and reoccur many times ascomponents of different tasks. A machine may be ableto learn how to tackle larger problems if it cansuccessfully find and exploit this repetition.Evidence suggests that a bottom up approach, thatrecursively finds building-blocks at one level ofabstraction and uses them at the next level, makeslearning in many complex environments tractable.This book describes a machine learning algorithmcalled HEXQ that automatically discovershierarchical structure in its environment purelythrough sense-act interactions, setting its own sub-goals and solving decision problems usingreinforcement learning.
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