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Partially Observable Markov Decision Processes (single agent) Tools for visualizing and defining visualizations of states, episodes, value functions, and policies. Tools for setting up experiments with multiple learning algorithms and plotting the performance using multiple performance metrics.

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This in a nutshell is the partial observability problem that is inherent in reinforcement learning techniques. Addressing Partial Observability Using Long Short-Term Memory (LSTM) Networks. One strategy for addressing the partial observability problem (where information about the actual state of the environment is missing or noisy) is to use long short-term memory neural networks. In contrast to artificial feedforward neural networks which have a one-way flow of information from the input ...

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The Optimal Control of Partially Observable Markov Processes, PhD Thesis, Stanford University, 1971 V. Krishnamurthy. Partially Observed Markov Decision Processes: From Filtering to Controlled Sensing, Camb. Un. Press, 2016 Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An introduction, Second Edition, The MIT press, 2018 B. Hajek.

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" eBook Reinforcement Learning And Approximate Dynamic Programming For Feedback Control " Uploaded By Eiji Yoshikawa, reinforcement learning and approximate dynamic programming for feedback control edited by frank l lewis derong liu p cm isbn 978 1 118 10420 0 hardback 1 reinforcement learning 2 feedback control systems i

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Harnessing the transformer's ability to process long time horizons of information could provide a similar performance boost in partially observable reinforcement learning (RL) domains, but the large-scale transformers used in NLP have yet to be successfully applied to the RL setting.

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45 Reinforcement Learning 16/11/2009 Reinforcement Learning. 46 Reinforcement Learning Summary POMDPs compute the optimal action in partially observable, stochastic domains. For finite horizon problems, the resulting value functions are piecewise linear and convex.