Back Again to Monte Carlo
Last Updated on August 29, 2025 by Editorial Team
Author(s): Rem E
Originally published on Towards AI.
We will explore our second method for solving RL problems
We’re diving into our second method for solving RL problems: Monte Carlo (MC).
The article discusses the Monte Carlo method for solving reinforcement learning problems, comparing it with the traditional dynamic programming approach. It emphasizes the importance of learning through experience, where the agent improves its policy by generating complete episodes and averaging the returns from these episodes. Additionally, it elaborates on the two methods of Monte Carlo evaluation—first-visit and every-visit—and presents on-policy versus off-policy methods, illustrating how exploration can be balanced with exploitation in policy improvement to converge to the optimal solution.
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