1) Multi-agent deep reinforcement learning using environment states augmented by coordination features:
Multiple A3C agents for traffic signal control: https://youtu.be/9kLDqKnXGWQ.
Multiple cooperative A3C agents for traffic signal control: https://youtu.be/Lnh2vjQYPCU.
Two heterogeneous A3C agents in the Milk Factory environment: https://youtu.be/05g70va66mw.
Two heterogeneous cooperative A3C agents in the Milk Factory environment: https://youtu.be/AupSocLeYUY.
Three heterogeneous A3C agents in the Milk Factory environment: https://youtu.be/9QXS7G5h2CM.
Three heterogeneous cooperative A3C agents in the Milk Factory environment: https://youtu.be/OdSnHQ9EAfs.
2) Multi-objective deep reinforcement learning (MODRL):
A demonstration obtained from our MODRL framework using the 3-objective Mountain Car problem: https://youtu.be/r3TQbXfgaVM
A demonstration obtained from our MODRL framework using the deep-sea treasure 5-clolumn environment: https://youtube.com/shorts/J60iL2GQtBw
3) Multi-agent deep reinforcement learning (MADRL):Â
Two deep RL agents (in green and yellow) cooperating to play the Tank game in a multi-agent environment: https://youtu.be/_-6CqCsI6Tg
4) Hand object detection:
A woman dancing: https://youtu.be/CMq7392CIaY
Students raising hands in a class: https://youtu.be/yqArrU1nnfI
People in a conference room: https://youtu.be/MAn1YiwjarM
People playing chess in a courtyard: https://youtu.be/7-234tg3JLc
People playing cards in an office: https://youtu.be/ff_fIRxz2-M
A man greeting a group: https://youtu.be/1G063siNpQ4
A group of friends on truck: https://youtube.com/shorts/vj53Emxekfw