Reinforcement Learning: SARSA and Q-Learning — Part 3
Last Updated on September 7, 2023 by Editorial Team
Author(s): Tan Pengshi Alvin
Originally published on Towards AI.
Introducing the Temporal Difference family of iterative techniques to solve the Markov Decision Process
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Image by Alexey Savchenko on Unsplash
In the previous article — Part 2 — we discovered a few solution algorithms to solve the Markov Decision Process (MDP), namely the Dynamic Programming method and the Monte Carlo method. The Dynamic Programming approach can be easily applied when we know the entire environmental dynamics of the MDP, such as the Transitional Probabilities between all states (conditioned on actions). However, such assumptions may not be practical, especially when we consider real-world applications, when stochastic relationships between states and actions are often vague.
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Published via Towards AI