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Creating Next-Gen Video Game AI With Reinforcement Learning
Discover how reinforcement learning is being used to upend traditional methods for creating video game AI
Reinforcement learning stands to become the new gold standard in creating intelligent video game AI. The chief advantage of reinforcement learning(RL) over traditional game AI methods is that, rather than hand-crafting the AI’s logic using complicated behavior trees, with RL one simply rewards the behavior they wish the AI to manifest and the agent learns by itself to perform the necessary sequence of actions to achieve the desired behavior. In essence, this is how one might teach a dog to perform tricks using a food reward.
The RL approach to game AI can be used to train a variety of strategic behaviors, including path finding, NPC attack and defense, and almost every behavior a human is capable of exhibiting while playing a video game. State-of-the-art implementations include those used to defeat best in class human players at Chess, Go and multiplayer strategy video games. There are few limits on what strategic behaviors an RL algorithm can theoretically discover, however in practice, computational expense and environment complexity constrain the type of behaviors that one would want to implement using RL. It is therefore important to…
