  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.
