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28 lines
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28 lines
1.4 KiB
Plaintext
# Type of tasks [[tasks]]
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A task is an **instance** of a Reinforcement Learning problem. We can have two types of tasks: **episodic** and **continuing**.
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## Episodic task [[episodic-task]]
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In this case, we have a starting point and an ending point **(a terminal state). This creates an episode**: a list of States, Actions, Rewards, and new States.
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For instance, think about Super Mario Bros: an episode begin at the launch of a new Mario Level and ends **when you’re killed or you reached the end of the level.**
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<figure>
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<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/mario.jpg" alt="Mario">
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<figcaption>Beginning of a new episode.
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</figcaption>
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</figure>
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## Continuing tasks [[continuing-tasks]]
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These are tasks that continue forever (**no terminal state**). In this case, the agent must **learn how to choose the best actions and simultaneously interact with the environment.**
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For instance, an agent that does automated stock trading. For this task, there is no starting point and terminal state. **The agent keeps running until we decide to stop it.**
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<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/stock.jpg" alt="Stock Market" width="100%">
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To recap:
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<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/tasks.jpg" alt="Tasks recap" width="100%">
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