Extending Reinforcement Learning to Provide Dynamic Game Balancing

Gustavo Andrade Geber Ramalho 1 Hugo Santana Vincent Corruble 1
1 SMA - Systèmes Multi-Agents
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : A recognized major concern for the game developers’ community is to provide mechanisms to dynamically balance the difficulty level of the games in order to keep the user interested in playing. This work presents an innovative use of reinforcement learning to build intelligent agents that adapt their behavior in order to provide dynamic game balancing. The idea is to couple learning with an action selection mechanism which depends on the evaluation of the current user’s skills. To validate our approach, we applied it to a real-time fighting game, obtaining good results, as the adaptive agent is able to quickly play at the same level as opponents with different skills.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01493239
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Submitted on : Tuesday, March 21, 2017 - 11:12:25 AM
Last modification on : Thursday, March 21, 2019 - 1:06:52 PM

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  • HAL Id : hal-01493239, version 1

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Gustavo Andrade, Geber Ramalho, Hugo Santana, Vincent Corruble. Extending Reinforcement Learning to Provide Dynamic Game Balancing. IJCAI 2005 Workshop on Reasoning, Representation, and Learning in Computer Games, Jul 2005, Edinburgh, United Kingdom. pp.7-12. ⟨hal-01493239⟩

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