By Edward Robinson, Peter McBurney, Xin Yao (auth.), Peter Vrancx, Matthew Knudson, Marek Grześ (eds.)
This quantity constitutes the completely refereed post-conference complaints of the overseas Workshop on Adaptive and studying brokers, ALA 2011, held on the tenth foreign convention on self sufficient brokers and Multiagent structures, AAMAS 2011, in Taipei, Taiwan, in may perhaps 2011. The 7 revised complete papers provided including 1 invited speak have been conscientiously reviewed and chosen from a number of submissions. The papers are geared up in topical sections on unmarried and multi-agent reinforcement studying, supervised multiagent studying, edition and studying in dynamic environments, studying belief and recognition, minority video games and agent coordination.
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Additional info for Adaptive and Learning Agents: International Workshop, ALA 2011, Held at AAMAS 2011, Taipei, Taiwan, May 2, 2011, Revised Selected Papers
For attaining such an inter-State mapping a supervised learning algorithm should be used. The major problem for any function approximator is the missing correspondence between the inputs, being states in S2 to the outputs being states in S1 . We approach this problem by finding this correspondence between the inputs and the labels in a common task-subspace as described in Section 4. 1). 2 The framework is not limiting to having an optimal policy — we believe suboptimal policies could also be used successfully — but we focus on optimal policies for clarity of exposition.
Lauer and Riedmiller  prove that DQL converges to optimal joint policies for any cooperative multiagent Markov Decision Process (MAMDP) given that each state-action pair is visited inﬁnitely often. Since cooperative stage games are a special variant of cooperative MAMDPs with an empty state set, this result obviously also holds for cooperative stage games. e. repeatedly played cooperative common static games (cf. Def. 4). This gives us Corollary 1: Corollary 1. Distributed Q-Learning converges to optimal joint policies for cooperative common stage games given that each action is performed inﬁnitely often.
Knudson, and M. ): ALA 2011, LNCS 7113, pp. 37–53, 2012. c Springer-Verlag Berlin Heidelberg 2012 38 T. K. B¨ uning each particular state-action pair as well as a strategy for each state of a game. Clearly, this becomes problematic in complex and large systems and is known as curse of dimensionality . In general, MARL algorithms can be classiﬁed along several dimensions , including the amount of information exchange, agent knowledge, or task type to name a few. Agents can be classiﬁed into joint-action learners, independent learners or into a class in between .
Adaptive and Learning Agents: International Workshop, ALA 2011, Held at AAMAS 2011, Taipei, Taiwan, May 2, 2011, Revised Selected Papers by Edward Robinson, Peter McBurney, Xin Yao (auth.), Peter Vrancx, Matthew Knudson, Marek Grześ (eds.)