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Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
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Convex Optimization
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Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics)
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Algorithms for Reinforcement Learning
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Foundations of Modern Probability (Probability Theory and Stochastic Modelling, 99)
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An Introduction to Markov Processes (Graduate Texts in Mathematics, 230)
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Markov Chains: Gibbs Fields, Monte Carlo Simulation and Queues
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Reinforcement Learning and Optimal Control
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Rollout, Policy Iteration, and Distributed Reinforcement Learning
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Stochastic Differential Equations: An Introduction with Applications
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Algorithmic Game Theory
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Decision Making Under Uncertainty: Theory and Application (MIT Lincoln Laboratory Series)
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Approximation Algorithms
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Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis
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Probability: Theory and Examples (Cambridge Series in Statistical and Probabilistic Mathematics Book 49)
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Diffusions, Markov Processes, and Martingales: Volume 1, Foundations
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Diffusions, Markov Processes and Martingales: Volume 2, Itô Calculus
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