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Visualizing Cumulative Rewards: Comparing Exploration Strategies in Multi-Armed Bandit Ch. 6 8 дней назад


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Visualizing Cumulative Rewards: Comparing Exploration Strategies in Multi-Armed Bandit Ch. 6

In this part of the series, we visualize the performance of various Reinforcement Learning exploration strategies—Random Exploration, Greedy Exploration, Epsilon-Greedy Exploration, and Upper Confidence Bound (UCB) Exploration—by plotting their Cumulative Click-Through Rates (CTR). Through the use of Python and Matplotlib, we compute and plot the cumulative rewards over iterations for each strategy. The visualizations reveal how different approaches perform in terms of exploration versus exploitation, helping you understand which strategy yields better results over time. This section provides a hands-on way to compare RL techniques, giving you insights into how to effectively model and analyze different exploration strategies in reinforcement learning tasks.

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