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Learn decision making under uncertainty through a comprehensive tutorial on the POMDPs.jl framework and its ecosystem in the Julia programming language. Master Markov Decision Processes (MDPs) as foundational concepts before advancing to Partially Observable Markov Decision Processes (POMDPs) for handling incomplete information scenarios. Explore state estimation techniques using particle filtering methods to track system states when observations are limited or noisy. Discover approximate solution methods for solving complex POMDPs when exact solutions are computationally intractable. Dive into deep reinforcement learning approaches that combine neural networks with POMDP frameworks for handling high-dimensional state and observation spaces. Examine imitation learning techniques for learning policies from expert demonstrations when reward functions are difficult to specify. Conclude with black-box validation methods to evaluate and verify the performance of POMDP-based decision-making systems in real-world applications.
