8: Markov Decision Process (MDP)
The Markov Decision Process (MDP) is one of the most basic blocks of reinforcement learning architectures. A conceptual algorithm in its own right, it has been adapted into a great number of other algorithms, and recurs frequently in the current crop of AI/ML research.
MDP explores a data environment by using its evaluation of its current state (i.e. ‘where’ it is in the data) to decide which node of the data to explore next.
Besides its obvious applicability to chess and other strictly sequential games, MDP is also a natural contender for the procedural training of robotics systems.
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- Supervised learning: It has a set of labelled data to train the model on. In a way, it means supervising a machine by providing a ton of information about a particular case and giving it the case outcome.
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