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Reinforcement Learning: Deep Q Networks

Towards Data Science 12:52 pm on May 23, 2024

The text discusses training and analyzing DQN models in reinforcement learning environments like LunarLander-v2, using hyperparameters optimized after 1000 episodes. It emphasizes the progression from initial clumsy decision-making to more strategic, efficient actions as training progresses.

<h4> Overview of DQN Agent Training
<h3> Initial Performance (first 10 episodes)
<p> Improvements noted in subsequent training sessions after extensive practice.
<strong><i><span style="color:blue;">Experimentation Recommendations<h3> Conclusion and Future Work Suggestions

https://towardsdatascience.com/reinforcement-learning-from-scratch-deep-q-networks-0a8d33ce165b

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Reinforcement Learning: Deep Q Networks

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