Reinforcement Learning: Function Approximation and Deep Q-Networks — Part 4
Last Updated on November 6, 2023 by Editorial Team
Author(s): Tan Pengshi Alvin
Originally published on Towards AI.
Reinforcement Learning with continuous state spaces and gradient descent techniques
Image by SpaceX on Unsplash
Since Part 1 of this series, we have framed Reinforcement Learning as a Markov Decision Process environment with discrete states and actions with their corresponding state-action values Q(s,a) stored in a tabular manner. This means that you can imagine a 2-dimensional table, with a row index presented by states and a column index represented by actions. The table entries are then the array of Q(s,a) values. This simplified paradigm has allowed us to gain intuition about how a decision-making agent learns as it traverses through a bite-sized environment.
In real-world applications, however, there can either be an… Read the full blog for free on Medium.
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