Firefly
发表于 2025-3-23 11:21:40
hat sich im Laufe der Jahre verschoben. Zugespitzt gesagt: Trotz divergierender Auffassungen in einzelnen Punkten schien in den frühen Jahren der Frauenbewegung das Subjekt der Bewegung klar („Wir Frauen“) und in der Solidaritäts-Diskussion ging es schwerpunktmäßig um die eher praktische Frage der
commodity
发表于 2025-3-23 16:40:18
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dry-eye
发表于 2025-3-23 18:11:05
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山间窄路
发表于 2025-3-23 23:04:33
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切碎
发表于 2025-3-24 04:29:35
Maximizing Social Welfare in the Presence of Markovian Agents,ves close to the best response with a high probability against a set of memory-bounded agents whose memory size is upper-bounded by a known value, and achieves close to the security value against any other set of agents which cannot be represented as being .. memory-bounded. . is the first MAL algor
镇压
发表于 2025-3-24 07:00:19
Targeted Modeling of Markovian Agents,y close to the SW maximizing joint return by exploiting the Markovian agents maximally, in efficient sample complexity. We assume that . has some prior knowledge of the possible set of features . upon which the Markovian agents may base their policies, but not the exact set.
dissolution
发表于 2025-3-24 11:32:25
Structure Learning in Factored MDPs,ent. Both of these subroutines assume prior knowledge of the possible set of features upon which a Markovian agent may base its policy, but not the exact set. For both of these subroutine, we pose the problem of modeling the unknown policy of a Markovian agent as learning the unknown feature space a
POWER
发表于 2025-3-24 18:00:56
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SHRIK
发表于 2025-3-24 21:39:55
Conclusion and Future Work,uman supervision. Two important capabilities in service of this goal are learning and interaction. Learning is necessary because agent developers cannot be expected to predict the characteristics of all possible environments that the agent might come across in the future. Rather, when situated in a
obviate
发表于 2025-3-25 00:28:06
Structure Learning in Factored MDPs,act set. For both of these subroutine, we pose the problem of modeling the unknown policy of a Markovian agent as learning the unknown feature space and transition function of an induced MDP (induced by the Markovian agent’s policy).