Reinforcement Learning and Deep RL Python Theory and Projects - Implementing Frozen Lake - 2

The video tutorial covers setting hyperparameters for a reinforcement learning agent. It explains the significance of total episodes, learning rate, max steps, gamma (discount factor), epsilon, and decay rate. The tutorial emphasizes the balance between exploration and exploitation and prepares for the next video on implementing the agent using Gym.

Describe the relationship between exploration and exploitation in reinforcement learning.

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How does the maximum value of epsilon affect the learning process?

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