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Reinforcement learning in commercial computer games

McGill University
Publication Date
  • Reinforcement Learning.
  • Computer Games.


We have created three experimental bots: ChaserBot, ItemBot and HybridBot. The two first bots each focus on a different aspect of the first-person shooter genre, and learn using basic RL. ChaserBot learns to chase down and shoot an enemy player. ItemBot, on the other hand, learns how to pick up the items---weapons, ammunition, armor---that are available, scattered on the ground, for the players to improve their arsenal. Both of these bots become reasonably proficient at their assigned task. Our goal for the third bot, HybridBot, was to create a bot that both chases and shoots an enemy player and goes after the items in the environment. Unlike the two previous bots which only have primitive actions available (strafing right or left, moving forward or backward, etc.), HybridBot uses options. At any state, it may choose either the player chasing option or the item gathering option. These options' internal policies are determined by the data learned by ChaserBot and ItemBot. HybridBot uses reinforcement learning to learn which option to pick at a given state.

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