Riot Games and deep reinforcement learning in gaming

At a recent Production RL Summit, Ben Kasper from Riot Games discussed how reinforcement learning (RL) has improved game balance in games such as Legends of Runeterra and DotA 2. By training an agent to play itself, Riot Games was able to identify issues that could ruin the game experience for players. The company employed a straightforward RL recipe that considered future variations in gameplay and captured patterns in game states. This led to the discovery of specific cards that were too strong or weak, allowing designers to balance the decks. The experiment showed promising results, with the RL-generated metrics matching design intuition and predicting the strongest deck. Riot Games has since productionized the process with an API, app, monitoring, and dashboard for analysis, making game designer KPIs include RL algorithm "balance" scores. This technology now enables designers to analyze and iterate on game balance prior to content release, and the company continues to update its tech stack to meet future challenges.

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