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Last Updated on August 29, 2025 by Editorial Team
Author(s): Rem E
Originally published on Towards AI.
Tutorial 7: Implementing Dynamic Programming for our maze problem
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This article explains how to implement Dynamic Programming (DP) to enable a reinforcement learning agent to successfully navigate a maze, building on concepts from previous tutorials. It introduces the project setup, focuses on the agent class implementation, and walks through both the theoretical and practical aspects of applying DP to optimize the agent’s decision-making process. Finally, it emphasizes understanding the underlying mathematics and algorithms that inform the agent’s learning while hinting at future explorations of partially observable environments.
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