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Deep Reinforcement Learning

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Open source interface to reinforcement learning tasks, different environments

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Elementary Reinforcement Learning and Reinforcement Learning in Practice

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Key ideas and algorithms of reinforcement learning, the field's intellectual foundations, recent developments and applications

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present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning

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provide a clear and simple account of the key ideas and algorithms of reinforcement learning

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Mathematical emphasis on RL

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Statistical Markov model in which the system being modeled is assumed to be a Markov process with hidden states.

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Statistical Markov model in which the system being modeled is assumed to be a Markov process with hidden states.

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Mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker; the discrete stochastic version of the optimal control problem

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a model-free reinforcement learning algorithm; to learn a policy, which tells an agent what action to take under what circumstances

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methods comprise a class of algorithms for sampling from a probability distribution.

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Elementary Reinforcement Learning and Reinforcement Learning in Practice

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*
*

Key ideas and algorithms of reinforcement learning, the field's intellectual foundations, recent developments and applications

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*
*

present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning

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*
*

provide a clear and simple account of the key ideas and algorithms of reinforcement learning

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*
*

Mathematical emphasis on RL

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Camillo Rohe

Reinforcement Learning