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EN ARBEJDSPLADSROMAN FRA DET 22. ÅRHUNDREDE Besætningen på Det sekstusinde skib består af menneskelige og menneskelignende ansatte. Da skibet får en række genstande ombord fra planeten Nyopdagelsen, begynder de ansatte at udvikle dybe forbindelser til genstandene, men uden helt at kunne forstå hvorfor. Snart ser besætningen sig selv i et nyt lys, og enhver ansat må spørge sig selv om arbejdsgangen kan fortsætte som før. Opbygget som en protokol med vidneudsagn forsøger den litterære science-fiction roman De ansatte at undersøge, hvad forskellen er på et menneske og en ting, og hvad det vil sige at være i live.

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration