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This paper presents the first ever approach for solving continuous-observation Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) and their semi-Markovian counterparts, Dec-POSMDPs. This contribution is especially important in robotics, where a vast number of sensors provide continuous observation data. A continuous-observation policy representation is introduced using Stochastic...
This paper proposes a modified version of opinion communication based on naming game to simulate the formation of opinion in real society. In the version, freshness is brought in to measure memory level of one opinion for each agent, when various opinions exist in social population. Main researches are devoted to new opinion replacing old opinion after system already convergences to an opinion, revealing...
An off-policy Bayesian nonparameteric approximate reinforcement learning framework, termed as GPQ, that employs a Gaussian processes (GP) model of the value (Q) function is presented in both the batch and online settings. Sufficient conditions on GP hyperparameter selection are established to guarantee convergence of off-policy GPQ in the batch setting, and theoretical and practical extensions are...
According to game theory, the altruistic behavior on complex network is researched by a mechanism Based on reputation and future expectation we proposed. As the information of players' historical behavior, reputation is an important foundation while players choose opponent. The players not only consider current payoff but also care about future payoff when they employ strategy. Simulations and analyses...
The blind multi-channels identification problem is studied in this paper. A cost function based on the orthogonal property between the output autocorrelation matrix and the channels parameter matrix is first constructed for a signal-input multiple-output FIR system. Then, an improved particle swarm optimizer, in which the personal best particle is replaced with the weight average of personal best...
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