  This paper delves into applying reinforcement learning (RL) in strategy
games, particularly those characterized by parity challenges, as seen in
specific positions of Go and Chess and a broader range of impartial games. We
propose a simulated learning process, structured within a curriculum learning
framework and augmented with noisy labels, to mirror the intricacies of
self-play learning scenarios. This approach thoroughly analyses how neural
networks (NNs) adapt and evolve from elementary to increasingly complex game
positions. Our empirical research indicates that even minimal label noise can
significantly impede NNs' ability to discern effective strategies, a difficulty
that intensifies with the growing complexity of the game positions. These
findings underscore the urgent need for advanced methodologies in RL training,
specifically tailored to counter the obstacles imposed by noisy evaluations.
The development of such methodologies is crucial not only for enhancing NN
proficiency in strategy games with significant parity elements but also for
broadening the resilience and efficiency of RL systems across diverse and
complex environments.
