October 2017
Beginner to intermediate
270 pages
7h
English
Content preview from Machine Learning for Developers
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Optimizing the Markov process
Reinforcement learning is an iterative interaction between an agent and the environment. The following occurs at each timestep:
- The process is in a state and the decision-maker may choose any action that is available in that state
- The process responds at the next timestep by randomly moving into a new state and giving the decision-maker a corresponding reward
- The probability that the process moves into its new state is influenced by the chosen action in the form of a state transition function
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