Author: PyData
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PyData Madrid 2016 What is reinforcement learning and when is it useful? How can it be implemented and applied in Python? This talk will attempt to answer these questions. This will be accomplished in three main sections - an overview of reinforcement learning and its applications, implementation details of the algorithm using Python, and a Python demo of reinforcement learning applied to a real problem. 1) Introduction & Concepts ### Definition of Reinforcement Learning ### Relevant Concepts * Markov decision process * state space * action space * transition probabilities * state rewards * policy ### Using Data * estimating transition probabilities and rewards from data * identifying the optimal policy given estimated transition probabilities 2) Implementation in Python Code sample: estimating the state-transition-probability tensor Code sample: estimating rewards Code sample: iteratively solving for the optimal policy 3) Demo example problem demo use of a reinforcement learning to solve in Python