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We propose a novel reinforcement learning algorithm, AlphaNPI, thatincorporates the strengths of Neural Programmer-Interpreters (NPI) andAlphaZero. NPI contributes structural biases in the form of modularity,hierarchy and recursion, which are helpful to reduce sample complexity, improvegeneralization and increase interpretability. AlphaZero contributes powerfulneural network guided search algorithms, which we augment with recursion.AlphaNPI only assumes a hierarchical program specification with sparse rewards:1 when the program execution satisfies the specification, and 0 otherwise.Using this specification, AlphaNPI is able to train NPI models effectively withRL for the first time, completely eliminating the need for strong supervisionin the form of execution traces. The experiments show that AlphaNPI can sort aswell as previous strongly supervised NPI variants. The AlphaNPI agent is alsotrained on a Tower of Hanoi puzzle with two disks and is shown to generalize topuzzles with an arbitrary number of disk

Learning Compositional Neural Programs with Recursive Tree Search and Planning