Titlebook: Adaptive and Learning Agents; AAMAS 2011 Internati Peter Vrancx,Matthew Knudson,Marek Grześ Conference proceedings 2012 Springer-Verlag Gmb

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https://doi.org/10.1007/978-3-319-97454-5 interaction is required, but several timesteps before this is reflected in the reward signal. In these states, the algorithm will augment the state information to include information about other agents which is used to select actions. The techniques presented in this paper are the first to explicit

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Solving Sparse Delayed Coordination Problems in Multi-Agent Reinforcement Learningbutors to this volume offer useful typologies of knowledge brokerage and explicate the range of causal mechanisms that enable knowledge brokers’ influence on policymaking. The work included in this volume respo978-3-030-78757-8978-3-030-78755-4

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Front Matterironment, the operational aspects of using management platforms, the development environment, which con­ sists of software toolkits that are used to build management applications, the imple­ mentation environment, which deals with testing interoperability aspects of using management platforms, and o

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参考书目

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Multi-agent Reinforcement Learning for Simulating Pedestrian NavigationThe result that . algorithm is ε-optimal only says that if λ is sufficiently small then with probability arbitrarily close to unity, the algorithm converges to the optimal action. As we have seen in Chapters 2 and 3, all convergence results hold only when λ is sufficiently small. Small value of λ im

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Leveraging Domain Knowledge to Learn Normative Behavior: A Bayesian Approachnt has been successful in reinterpreting the scope of its liberal economic reforms, and with the dynamics that have gradually shaped the relationships between the government and some leading entrepreneurs of the Tunisian manufacturing industry since the early 1990s, while redefining the patterns of