000 04503nam a22005415i 4500
001 978-3-031-01591-5
003 DE-He213
005 20240730163633.0
007 cr nn 008mamaa
008 220601s2021 sz | s |||| 0|eng d
020 _a9783031015915
_9978-3-031-01591-5
024 7 _a10.1007/978-3-031-01591-5
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aLeno da Silva, Felipe.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979659
245 1 0 _aTransfer Learning for Multiagent Reinforcement Learning Systems
_h[electronic resource] /
_cby Felipe Leno da Silva, Anna Helena Reali Costa.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXVII, 111 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
505 0 _aPreface -- Acknowledgments -- Introduction -- Background -- Taxonomy -- Intra-Agent Transfer Methods -- Inter-Agent Transfer Methods -- Experiment Domains and Applications -- Current Challenges -- Resources -- Conclusion -- Bibliography -- Authors' Biographies .
520 _aLearning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and collaborate with other humans towards a common goal. Artificial Intelligent agents are like humans in this aspect. Reinforcement Learning (RL) is a well-known technique to train autonomous agents through interactions with the environment. Unfortunately, the learning process has a high sample complexity to infer an effective actuation policy, especially when multiple agents are simultaneously actuating in the environment. However, previous knowledge can be leveraged to accelerate learning and enable solving harder tasks. In the same way humans build skills and reuse them by relating different tasks, RL agents might reuse knowledge from previously solved tasks and from the exchange of knowledge with other agents in the environment. In fact, virtually all of the most challenging tasks currently solved by RL rely on embedded knowledge reuse techniques, such as Imitation Learning, Learning from Demonstration, and Curriculum Learning. This book surveys the literature on knowledge reuse in multiagent RL. The authors define a unifying taxonomy of state-of-the-art solutions for reusing knowledge, providing a comprehensive discussion of recent progress in the area. In this book, readers will find a comprehensive discussion of the many ways in which knowledge can be reused in multiagent sequential decision-making tasks, as well as in which scenarios each of the approaches is more efficient. The authors also provide their view of the current low-hanging fruit developments of the area, as well as the still-open big questions that could result in breakthrough developments. Finally, the book provides resources to researchers who intend to join this area or leverage those techniques, including a list of conferences, journals, and implementation tools. This book will be useful for a wide audience; and will hopefully promote new dialogues across communities and novel developments in the area.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_979660
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aCosta, Anna Helena Reali.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979661
710 2 _aSpringerLink (Online service)
_979662
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000362
776 0 8 _iPrinted edition:
_z9783031004636
776 0 8 _iPrinted edition:
_z9783031027192
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_979663
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01591-5
912 _aZDB-2-SXSC
942 _cEBK
999 _c84822
_d84822