In video games, \non-player characters (NPCs) are used to enhance the players' experience in a variety of ways, e.g., as enemies, allies, or innocent bystanders. A crucial component of NPCs is navigation, which allows them to move from one point to another on the map. The most popular approach for NPC navigation in the video game industry is to use a navigation mesh (NavMesh), which is a graph representation of the map, with nodes and edges indicating traversable areas. Unfortunately, complex navigation abilities that extend the character's capacity for movement, e.g., grappling hooks, jetpacks, teleportation, or double-jumps, increase the complexity of the NavMesh, making it intractable in many practical scenarios. Game designers are thus constrained to only add abilities that can be handled by a NavMesh. As an alternative to the NavMesh, we propose to use Deep Reinforcement Learning (Deep RL) to learn how to navigate 3D maps in video games using any navigation ability. We test our approach on complex 3D environments that are notably an order of magnitude larger than maps typically used in the Deep RL literature. One of these environments is from a recently released AAA video game called Hyper Scape. We find that our approach performs surprisingly well, achieving at least 90% success rate in a variety of scenarios using complex navigation abilities.
Distilling Reinforcement Learning Tricks for Video Games
