By Marc Ponsen, Matthew E. Taylor, Karl Tuyls (auth.), Matthew E. Taylor, Karl Tuyls (eds.)
ThisbookpresentsselectedandrevisedpapersoftheSecondWorkshoponAd- tive and studying brokers 2009 (ALA-09), held on the AAMAS 2009 convention in Budapest, Hungary, may perhaps 12. The goalof ALA is to supply an interdisciplinaryforum for scientists from numerous ?elds similar to computing device technological know-how, biology, video game conception and economics. This year’s variation of ALA used to be the second one after the merger of the previous wo- retailers ALAMAS and ALAg. In 2008 this joint workshop used to be equipped for the ?rst time less than the ?ag of either occasions. ALAMAS used to be a each year returning ecu- pean workshop on adaptive and studying brokers and multi-agent structures (held 8 times). ALAg was once the overseas workshop on adaptive and studying brokers, which was once frequently held at AAMAS. to extend the energy, visibility and caliber of the workshop it used to be determined to merge either workshops less than the ?ag of ALA and to establish a steerage Committee as an organizational spine. This booklet includes six papers offered in the course of the workshop, that have been conscientiously chosen after an extra evaluation around in the summertime of 2009. We for that reason desire to explicitly thank the participants of this system Committee for the standard and sincerity in their e?orts and repair. in addition we want to thank all of the individuals of the senior steerage Committee for making this workshop attainable and helping it with sound suggestion. We additionally thank the AAMAS convention for supplying us a platform for containing this occasion. eventually we additionally desire to thank all authors who spoke back to our call-for-papers with fascinating contributions.
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Additional info for Adaptive and Learning Agents: Second Workshop, ALA 2009, Held as Part of the AAMAS 2009 Conference in Budapest, Hungary, May 12, 2009. Revised Selected Papers
895–900 (2007) 23. : Transfer of samples in batch reinforcement learning. In: Proceedings of the 25th Annual ICML, pp. 544–551 (2008) 24. : Towards a unified theory of state abstraction for MDPs. In: Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics, pp. 531–539 (2006) 25. : Proto-value functions: A Laplacian framework for learning representation and control in Markov decision processes. Journal of Machine Learning Research 8, 2169–2231 (2007) 26. : Inductive logic programming: Theory and methods.
3 state variable dist(K1 , C) dist(K1 , K2 ) dist(K1 , K3 ) dist(K1 , K4 ) dist(K1 , T1 ) dist(K1 , T1 ) dist(K1 , T2 ) dist(K1 , T2 ) dist(K1 , T3 ) dist(K2 , C) dist(K2 , C) dist(K3 , C) dist(K3 , C) dist(K4 , C) dist(T1 , C) dist(T1 , C) dist(T2 , C) dist(T2 , C) dist(T3 , C) Min(dist(K2 , T1 ), dist(K2 , T2 )) Min(dist(K2 , T1 ), dist(K2 , T2 ), dist(K2 , T3 )) Min(dist(K3 , T1 ), dist(K3 , T2 )) Min(dist(K3 , T1 ), dist(K3 , T2 ), dist(K3 , T3 )) Min(dist(K4 , T1 ), dist(K4 , T2 ), dist(K4 , T3 )) Min(ang(K2 , K1 , T1 ), ang(K2 , K1 , T2 )) Min(ang(K2 , K1 , T1 ), ang(K2 , K1 , T2 ), ang(K2 , K1 , T3 ) Min(ang(K3 , K1 , T1 ), ang(K3 , K1 , T2 )) Min(ang(K3 , K1 , T1 ), ang(K3 , K1 , T2 ), ang(K3 , K1 , T3 )) Min(ang(K4 , K1 , T1 ), ang(K4 , K1 , T2 ), ang(K4 , K1 , T3 )) 3 vs.
By generalizing over different target task state variables and actions, the TL method can initialize all target task Q-values to values learned in the source task, biasing learning and resulting in significantly faster learning. The Value Function Transfer method in Table 2uses inter-task mappings (columns four and five), and outperforms Q-value Reuse, which had ignored novel state variables and actions. While inter-task mappings are a convenient way to fully specify relationships between MDPs, it is possible that not enough information is known to design a full 26 M.
Adaptive and Learning Agents: Second Workshop, ALA 2009, Held as Part of the AAMAS 2009 Conference in Budapest, Hungary, May 12, 2009. Revised Selected Papers by Marc Ponsen, Matthew E. Taylor, Karl Tuyls (auth.), Matthew E. Taylor, Karl Tuyls (eds.)