Towards a multi-agent non-player character road network: a Reinforcement Learning approach
2021 IEEE CONFERENCE ON GAMES (COG)(2021)
摘要
Creating detailed and interactive game environments is an area of great importance in the video game industry. This includes creating realistic Non-Player Characters which respond seamlessly to the players actions. Machine learning had great contributions to the area, overcoming scalability and robustness shortcomings of hand-scripted models. We introduce the early results of a reinforcement learning approach in building a simulation environment for heterogeneous, multi-agent nonplayer characters in a dynamic road network game scene.
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关键词
reinforcement learning, multi-agent, dynamic environment, Non-Player Characters, traffic simulation
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