Titlebook: Sample Efficient Multiagent Learning in the Presence of Markovian Agents; Doran Chakraborty Book 2014 Springer International Publishing Sw

Firefly

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 Solidarit?ts-Diskussion ging es schwerpunktm??ig um die eher praktische Frage der

commodity

dry-eye

山間窄路

切碎

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

鎮(zhèn)壓

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

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

SHRIK

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

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).