Winning Isn't Everything: Training Human-Like Agents for Playtesting and Game AI

By Yunqi Zhao, Igor Borovikov, Ahmad Beirami, Jason Rupert, Caedmon Somers, Jesse Harder, F. Silva, John F. Kolen, Jervis Pinto, Reza Pourabolghasem and 6 more. 2019

10 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Hongyuan Zha
2. Jiachen Yang
3. Chris H. Q. Ding
4. Deng Cai
5. Hui Han
6. Francis R. Bach
7. Wei Xu
8. Feiping Nie

Citations in the corpus

• Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery (2019, background): “From the viewpoint of human-AI cooperation, which has near-term application to video game AI to improve human players’ experiences [46], hierarchical policies trained with explicit skills is a key step toward interpretable and modular policies.”
• On Multi-Agent Learning in Team Sports Games (2019, unknown): “We build on a variety of planning methods and machine learning techniques (including the state-of-the-art deep RL) and move away from the recent trends at training superhuman agents in solving the game AI problem (Zhao et al., 2019).”