Titlebook: Recent Advances in Reinforcement Learning; Leslie Pack Kaelbling Book 1996 Springer Science+Business Media New York 1996 Performance.algor

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Stress-Fracture

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textile

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Efficient Reinforcement Learning through Symbiotic Evolution,ut loss of generalization. Such efficient learning, combined with few domain assumptions, make SANE a promising approach to a broad range of reinforcement learning problems, including many real-world applications.

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The Loss from Imperfect Value Functions in Expectation-Based and Minimax-Based Tasks,d in the context of expectation-based Markov decision problems. Our analysis generalizes this work to minimax-based Markov decision problems, yields new results for expectation-based tasks, and shows how minimax-based and expectation-based Markov decision problems relate.

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Technical Note,he non-Markovian effect of coarse state-space quantization. The resulting algorithm, ., thus combines some of the best features of the Q-learning and actor-critic learning paradigms. The behavior of this algorithm has been demonstrated through computer simulations.

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cumulative

are well known in numerous neutral or ionized atoms: 2.2. . . ., 2.2. . ., 2.2. . .. According to a recent interpretation(.) the . term of the hydrogen . is formally analogous to these terms and should be precisely assigned to the configuration (2.).Σ.(⋆). The analogy, however, breaks down in regar