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001440818 020__ $$a9783030893705$$q(electronic bk.)
001440818 020__ $$a3030893707$$q(electronic bk.)
001440818 020__ $$z9783030893699
001440818 0247_ $$a10.1007/978-3-030-89370-5$$2doi
001440818 035__ $$aSP(OCoLC)1283860132
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001440818 049__ $$aISEA
001440818 050_4 $$aQ334$$b.P33 2021
001440818 08204 $$a006.3$$223
001440818 1112_ $$aPacific Rim International Conference on Artificial Intelligence$$n(18th :$$d2021 :$$cOnline)
001440818 24510 $$aPRICAI 2021 : trends in artificial intelligence :$$b18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021, Hanoi, Vietnam, November 8-12, 2021 : proceedings.$$nPart III /$$cDuc Nghia Pham, Thanaruk Theeramunkong, Guido Governatori, Fenrong Liu (eds.).
001440818 264_1 $$aCham :$$bSpringer,$$c[2021]
001440818 264_4 $$c©2021
001440818 300__ $$a1 online resource (457 pages) :$$billustrations (chiefly color)
001440818 336__ $$atext$$btxt$$2rdacontent
001440818 337__ $$acomputer$$bc$$2rdamedia
001440818 338__ $$aonline resource$$bcr$$2rdacarrier
001440818 347__ $$atext file
001440818 347__ $$bPDF
001440818 4901_ $$aLecture notes in computer science. Lecture notes in artificial intelligence ;$$v13033
001440818 4901_ $$aLNCS sublibrary: SL7 - Artificial intelligence
001440818 500__ $$aInternational conference proceedings.
001440818 500__ $$aConference held in a virtual format.
001440818 500__ $$aIncludes author index.
001440818 5058_ $$aIntro -- Preface -- Organization -- Contents -- Part III -- Reinforcement Learning -- Consistency Regularization for Ensemble Model Based Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Method -- 4.1 Model Discrepancy and Consistency -- 4.2 Model Learning -- 4.3 Implementation -- 5 Experiments -- 5.1 Comparative Evaluation -- 5.2 Effects of Consistency Regularization -- 5.3 Ablation Study -- 6 Conclusions -- References -- Detecting and Learning Against Unknown Opponents for Automated Negotiations -- 1 Introduction -- 2 Related Work -- 3 Preliminaries
001440818 5058_ $$a3.1 Negotiation Settings -- 3.2 Bayes Policy Reuse -- 4 Agent Design -- 4.1 Deep Reinforcement Learning Based Learning Module -- 4.2 Policy Reuse Mechanism -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Performance Against ANAC Winning Agents -- 5.3 New Opponent Detection and Learning -- 6 Conclusion -- References -- Diversity-Based Trajectory and Goal Selection with Hindsight Experience Replay -- 1 Introduction -- 2 Background -- 2.1 Reinforcement Learning -- 2.2 Goal-Oriented Reinforcement Learning -- 2.3 Deep Deterministic Policy Gradient -- 2.4 Determinantal Point Processes
001440818 5058_ $$a3 Related Work -- 4 Methodology -- 4.1 Diversity-Based Trajectory Selection -- 4.2 Diversity-Based Goal Selection -- 5 Experiments -- 5.1 Environments -- 5.2 Training Settings -- 5.3 Benchmark Results -- 5.4 Ablation Studies -- 5.5 Time Complexity -- 6 Conclusion -- References -- Off-Policy Training for Truncated TD() Boosted Soft Actor-Critic -- 1 Introduction -- 2 Related Work -- 2.1 TD Learning and Multi-step Methods -- 2.2 TD() and Eligibility Traces -- 3 Preliminaries -- 3.1 MDPs and Temporal Difference Learning -- 3.2 Multi-step Algorithms and TD()
001440818 5058_ $$a4 Soft Actor-Critic with Truncated TD () -- 4.1 Off-Policy Truncated TD() -- 4.2 Soft Actor-Critic with Truncated TD() -- 4.3 SAC() Training -- 5 Experiments -- 5.1 Evaluation of SAC() -- 5.2 Ablation Study -- 6 Discussion -- References -- Adaptive Warm-Start MCTS in AlphaZero-Like Deep Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Warm-Start AlphaZero Self-play -- 3.1 The Algorithm Framework -- 3.2 MCTS -- 3.3 MCTS Enhancements -- 4 Adaptive Warm-Start Switch Method -- 5 Experimental Setup -- 6 Results -- 6.1 MCTS Vs MCTS Enhancements -- 6.2 Fixed I Tuning
001440818 5058_ $$a6.3 Adaptive Warm-Start Switch -- 7 Discussion and Conclusion -- References -- Batch-Constraint Inverse Reinforcement Learning -- 1 Introduction -- 2 Offline Inverse Reinforcement Learning -- 3 Method -- 3.1 Feature Expectation Approximation -- 3.2 Policy Optimization with BRL -- 3.3 Batch-Constraint Inverse Reinforcement Learning Algorithm (BCIRL) -- 4 Experiments -- 4.1 Standard Control Environments -- 4.2 Gridworld Example -- 5 Conclusion -- References -- KG-RL: A Knowledge-Guided Reinforcement Learning for Massive Battle Games -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Rule-Mix -- 3.2 Plan-Extend.
001440818 506__ $$aAccess limited to authorized users.
001440818 520__ $$aThis three-volume set, LNAI 13031, LNAI 13032, and LNAI 13033 constitutes the thoroughly refereed proceedings of the 18th Pacific Rim Conference on Artificial Intelligence, PRICAI 2021, held in Hanoi, Vietnam, in November 2021. The 93 full papers and 28 short papers presented in these volumes were carefully reviewed and selected from 382 submissions. PRICAI covers a wide range of topics in the areas of social and economic importance for countries in the Pacific Rim: artificial intelligence, machine learning, natural language processing, knowledge representation and reasoning, planning and scheduling, computer vision, distributed artificial intelligence, search methodologies, etc. Part III includes two thematic blocks: Reinforcement Learning, followed by Vision and Perception.
001440818 588__ $$aDescription based on print version record.
001440818 650_0 $$aArtificial intelligence$$vCongresses.
001440818 650_6 $$aIntelligence artificielle$$vCongrès.
001440818 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001440818 655_7 $$aConference papers and proceedings.$$2lcgft
001440818 655_7 $$aActes de congrès.$$2rvmgf
001440818 655_0 $$aElectronic books.
001440818 7001_ $$aPham, Duc-Nghia,$$eeditor.
001440818 7001_ $$aTheeramunkong, Thanaruk,$$eeditor.
001440818 7001_ $$aGovernatori, Guido,$$eeditor.
001440818 7001_ $$aLiu, Fenrong,$$eeditor.
001440818 77608 $$iPrint version:$$aPham, Duc Nghia.$$tPRICAI 2021: Trends in Artificial Intelligence.$$dCham : Springer International Publishing AG, ©2021$$z9783030893699
001440818 830_0 $$aLecture notes in computer science.$$pLecture notes in artificial intelligence.
001440818 830_0 $$aLecture notes in computer science ;$$v13033.
001440818 830_0 $$aLNCS sublibrary.$$nSL 7,$$pArtificial intelligence.
001440818 852__ $$bebk
001440818 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-89370-5$$zOnline Access$$91397441.1
001440818 909CO $$ooai:library.usi.edu:1440818$$pGLOBAL_SET
001440818 980__ $$aBIB
001440818 980__ $$aEBOOK
001440818 982__ $$aEbook
001440818 983__ $$aOnline
001440818 994__ $$a92$$bISE