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. ; This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.
Emotion Recognition from 3D Motion Capture Data using Deep CNNs
17.08.2021
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC: | 629 |
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