Mastering “Gongzhu” with Self-play Deep Reinforcement Learning

Wu, Licheng; Wu, Qifei; Zhong, Hongming; Li, Xiali

doi:10.1007/978-981-99-0617-8_11

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Playing Go Against Microbes1

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2024

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“…The second sub-system uses the personality extracted by the previous component to influence the final decision for the next action to perform. At the start of the process, KataGo (Wu, 2020) provides a raw policy which consists in non-zero probabilities for all legal moves. As such, an optimal player would greedily select the action with the highest probability for each turn.…”

Section: Playing Go Against Microbesmentioning

confidence: 99%

Communicating Through a Narrow Channel: Perceiving Microbial Affect Over a Game of Go

Caspar,

Fdez,

Chen

et al. 2024

Preprint

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Interfaces between agents may display special powers owing to their narrow bottlenecks in communication channels, such as games or other virtual media through which agents are given to interact. In this paper, we propose to derive an agent's personality from their behavior in the Game of Go as an arbitrary substrate for communication. Our approach is agnostic to the type of embodiment of the player, which means that it remains as applicable to humans as animals or simpler organisms, even artificial forms of intelligence. We base our methodology on related works characterizing affective states, including the seminal pleasure-arousal-dominance framework that focuses on modeling personalities. Our preliminary results indicate that narrow bottlenecks favor a reduction in the feature set necessary to characterize a personality type, thus making for more interesting social interactions in and around game-play.

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Section: Playing Go Against Microbesmentioning

confidence: 99%