Dynamic Programming in Reinforcement Learning

Author(s): Rem E

Originally published on Towards AI.

Our First Approach to Solving Reinforcement Learning Problems!

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The article explores dynamic programming in reinforcement learning, outlining its key principles and methods, including policy evaluation, policy improvement, and policy iteration. The author emphasizes the trade-offs between dynamic programming, Monte Carlo methods, and temporal difference learning, detailing how dynamic programming offers mathematical robustness but requires a complete model of the environment. Additionally, the article discusses the practical applications of these concepts and presents algorithms that can iteratively optimize decision-making processes in reinforcement learning scenarios, encouraging readers to further explore and engage with this complex yet essential topic in artificial intelligence.

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