  Recent advances in deep learning have allowed artificial agents to rival
human-level performance on a wide range of complex tasks; however, the ability
of these networks to learn generalizable strategies remains a pressing
challenge. This critical limitation is due in part to two factors: the opaque
information representation in deep neural networks and the complexity of the
task environments in which they are typically deployed. Here we propose a novel
Hierarchical Q-Network (HQN) motivated by theories of the hierarchical
organization of the human prefrontal cortex, that attempts to identify lower
dimensional patterns in the value landscape that can be exploited to construct
an internal model of rules in simple environments. We draw on combinatorial
games, where there exists a single optimal strategy for winning that
generalizes across other features of the game, to probe the strategy
generalization of the HQN and other reinforcement learning (RL) agents using
variations of Wythoff's game. Traditional RL approaches failed to reach
satisfactory performance on variants of Wythoff's Game; however, the HQN
learned heuristic-like strategies that generalized across changes in board
configuration. More importantly, the HQN allowed for transparent inspection of
the agent's internal model of the game following training. Our results show how
a biologically inspired hierarchical learner can facilitate learning abstract
rules to promote robust and flexible action policies in simplified training
environments with clearly delineated optimal strategies.
