Reinforcement Learning and Deep RL Python Theory and Projects - Replay Memory and Experience

The video tutorial explains the concept of replay memory in deep reinforcement learning, describing it as a list of experiences. Each experience is a tuple consisting of state, action, reward, and next state. The tutorial uses real-world examples, such as the stock market, to illustrate these concepts. It also discusses the finite capacity of replay memory and how to manage it by replacing old experiences with new ones when the memory is full.

How does the concept of experience relate to the actions taken in a given state?

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What is the significance of capacity in the context of replay memory?

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Explain the process of replacing experiences in replay memory when new experiences are added.

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