By Ana L. C. Bazzan, Denise de Oliveira (auth.), Karl Tuyls, Ann Nowe, Zahia Guessoum, Daniel Kudenko (eds.)
This booklet comprises chosen and revised papers of the ecu Symposium on Adaptive and studying brokers and Multi-Agent structures (ALAMAS), variations 2005, 2006 and 2007, held in Paris, Brussels and Maastricht. The objective of the ALAMAS symposia, and this linked publication, is to extend expertise and curiosity in model and studying for unmarried brokers and mul- agent platforms, and inspire collaboration among computing device studying specialists, softwareengineeringexperts,mathematicians,biologistsandphysicists,andgive a consultant overviewof present country of a?airs during this zone. it really is an inclusive discussion board the place researchers can current fresh paintings and speak about their most recent rules for a ?rst time with their friends. Thesymposiaseriesfocusesonallaspectsofadaptiveandlearningagentsand multi-agent structures, with a specific emphasis on the way to regulate verified studying recommendations and/or create new studying paradigms to deal with the various demanding situations awarded via advanced real-world difficulties. those symposia have been a good good fortune and supplied a discussion board for the pres- tation of recent principles and effects concerning the perception of version and studying for unmarried brokers and multi-agent structures. Over those 3 versions we acquired fifty one submissions, of which 17 have been conscientiously chosen, together with one invited paper of this year’s invited speaker Simon Parsons. it is a very c- petitive recognition cost of roughly 31%, which, including evaluation cycles, has ended in a superior LNAI quantity. we are hoping that our readers may be encouraged by way of the papers incorporated during this volume.
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Additional resources for Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning: 5th, 6th, and 7th European Symposium, ALAMAS 2005-2007 on Adaptive and Learning Agents and Multi-Agent Systems, Revised Selected Papers
When the action space is discrete, the approximation error of the resulting Q-function is bounded (9) and the sub-optimality of the resulting policy is also bounded (10) (the latter may be more relevant in practice). These bounds provide conﬁdence in the results of fuzzy Q-iteration. 36 6 L. Bu¸soniu et al. Example: 2-D Navigation In this section, fuzzy Q-iteration is applied to a two-dimensional (2-D) simulated navigation problem with continuous state and action variables. A point-mass with a unit mass value (1kg) has to be steered on a rectangular surface such that it gets close to the origin in minimum time, and stays there.
This is true thanks to the non-expansive nature of P and F , and because T is a contraction. It is shown next that asynchronous fuzzy Q-iteration (Algorithm 2) converges. The convergence proof is similar to that for exact asynchronous value iteration . Proposition 2. Asynchronous fuzzy Q-iteration (Algorithm 2) converges. Proof. Denote n = N · M , and rearrange the matrix θ into a vector in Rn , placing ﬁrst the elements of the ﬁrst row, then the second etc. The element at row i and column j of the matrix is now the l-th element of the vector, with l = (i − 1) · M + j.
Since the parameters are updated in an asynchronous fashion, this version is called asynchronous Q-iteration (in Algorithm 2 parameters are updated in sequence, but they can actually be updated in any order and our results still hold). Although the exact version of asynchronous Q-iteration is widely used [1, 2], the asynchronous variant has received little attention in the context of approximate RL. To diﬀerentiate between the two versions, Algorithm 1 is hereafter called synchronous fuzzy Q-iteration.