An embodied approach to the Tower of Hanoi
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2022-02-11
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en
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Abstract
Deep reinforcement techniques often simplify actions so that the actions
have a direct e ect on the task. In this thesis, an embodied approach is
taken instead to solve the Tower of Hanoi by having a robot learn to drive
around in ve di erent environments with di erent environmental and task
complexities. The robot should learn how moving actions in
uence the game
state. In the end, the agent is not able to learn this task even for the simplest
environment, and performs even worse for higher complexities, as expected.
These issues are hypothesized to be merely implementation speci c, and
with adaptations to the simulation and reward signal the agent should be
able to do better.
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Faculteit der Sociale Wetenschappen