An Adversarial Approach for Automated Pokémon Team Building and Metagame Balance

Metagame balance is a crucial task in game development, and automation of this process could assist game developers by vastly reducing time costs. We explore and evaluate a metagame balance model over the recently proposed VGC AI Competition Framework. We propose an adversarial model where team builder agents try to maximize their win rate by narrowing to the most optimal team configurations, resulting in a reduction of the diversity of
Pokémon
employed, while a balancing agent readapts the
Pokémon
inner attributes to incentivize the team builder agents to incorporate a greater variety of
Pokémon
into their teams increasing the metagame's overall diversity and balance. Furthermore, we develop multiple team builder agents divided into two groups: the first group assumes that individual
Pokémon
advantages are the primary factor to determine the outcome of game matches; the second group also exploits the implicit synergy between teammates. These agents make use of metagaming, linear optimization, and evolutionary search to find strong combinations against the current metagame. The strongest team builder is faced against the team metagame balance agent for its evaluation. Deep learning is also employed to predict the outcome of matches and recommend constructive elements of teams.