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arXiv · 1911.00926

Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer Architecture

Daniel Tanneberg·Elmar Rueckert·Jan Peters
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Abstract

A key feature of intelligent behavior is the ability to learn abstract strategies that transfer to unfamiliar problems. Therefore, we present a novel architecture, based on memory-augmented networks, that is inspired by the von Neumann and Harvard architectures of modern computers. This architecture enables the learning of abstract algorithmic solutions via Evolution Strategies in a reinforcement learning setting. Applied to Sokoban, sliding block puzzle and robotic manipulation tasks, we show that the architecture can learn algorithmic solutions with strong generalization and abstraction: scaling to arbitrary task configurations and complexities, and being independent of both the data representation and the task domain.

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Daniel Tanneberg, Elmar Rueckert, Jan Peters. 2020-06-03. Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer Architecture. https://arxiv.org/abs/1911.00926

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