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Session Name:

Machine Learning Summit: Creating Cooperative Character Behaviors Using Deep Reinforcement Learning


Creating cooperative character behaviors is very challenging and is costly to studios of all sizes. Deep reinforcement learning can be a great approach because a developer can articulate the desired objectives or goals and have the machines learn the best behavior. This can impact the economics of the development of the game. However, most DRL algorithms are intended to solve single character tasks (hence no cooperation between characters). However, there are new advancements in cooperative DRL tools, such as centralized critic, that can be leveraged to create cooperative character behaviors in a game. In this presentation, we will go over these topics and illustrate a real-life example of a studio using deep reinforcement learning to create cooperative characters in their game.

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