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CARL introduced as a reinforcement-learning agent for creating and steering self-organizing patterns

CARL's authors say the agent learns to create patterns in Lenia, steer their behavior and let people guide them in real time.

2 Sources, 21d ago, first seen 21d ago

TLDR

Announced on September 10, CARL is described by its authors as a closed-loop reinforcement-learning agent for discovering and controlling self-organizing phenomena in Lenia, with real-time human guidance. A quote-post calls the work a milestone for actively controlling such patterns to achieve goals.

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2 Sources, first seen 21d ago

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2 Sources, first seen 21d ago

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2 Sources

Bert Chan@BertChakovskyThis is an interesting milestone, showing RL can actively control self-organizing patterns like Lenia to achieve goals. Together with NCA+IMGEP (@hamongautier et al) & perceptual occlution (@jessescool_ et al) providing passive controls, we now have a variety of control methods.21d
Stefania Druga@Stefania_drugaRT @BertChakovsky: This is an interesting milestone, showing RL can actively control self-organizing patterns like Lenia to achieve goals.…21d
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    2 Sources

    Bert Chan@BertChakovskyThis is an interesting milestone, showing RL can actively control self-organizing patterns like Lenia to achieve goals. Together with NCA+IMGEP (@hamongautier et al) & perceptual occlution (@jessescool_ et al) providing passive controls, we now have a variety of control methods.21d
    Stefania Druga@Stefania_drugaRT @BertChakovsky: This is an interesting milestone, showing RL can actively control self-organizing patterns like Lenia to achieve goals.…21d
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