Deep Reinforcement Learning With Macro-Actions

Clemens Rosenbaum
Clemens Rosenbaum
Stefan Dernbach
Stefan Dernbach

arXiv: Learning, Volume abs/1606.04615, 2016.

Cited by: 11|Views64
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Abstract:

Deep reinforcement learning has been shown to be a powerful framework for learning policies from complex high-dimensional sensory inputs to actions in complex tasks, such as the Atari domain. In this paper, we explore output representation modeling in the form of temporal abstraction to improve convergence and reliability of deep reinforc...More

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