  World-class human players have been outperformed in a number of complex two
person games (Go, Chess, Checkers) by Deep Reinforcement Learning systems.
However, owing to tractability considerations minimax regret of a learning
system cannot be evaluated in such games. In this paper we consider simple
games (Noughts-and-Crosses and Hexapawn) in which minimax regret can be
efficiently evaluated. We use these games to compare Cumulative Minimax Regret
for variants of both standard and deep reinforcement learning against two
variants of a new Meta-Interpretive Learning system called MIGO. In our
experiments all tested variants of both normal and deep reinforcement learning
have worse performance (higher cumulative minimax regret) than both variants of
MIGO on Noughts-and-Crosses and Hexapawn. Additionally, MIGO's learned rules
are relatively easy to comprehend, and are demonstrated to achieve significant
transfer learning in both directions between Noughts-and-Crosses and Hexapawn.
