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Reinforcement Learning Analogy - Video Games
Video games often involve completing levels, with increasing difficulty as you go through the game. In each level, you go in completely blind and make decisions that will either lead to you passing that level or failing. If you fail, you will now understand more about that particular level that will influence your decisions when you retry. In other words, you will understand what not to do, and try another approach. If you pass, you will understand what to do in future levels. This process is repeated until success. This is very similar to how reinforcement learning works. Rewards are given for successes, indicating that the decision(s) made are correct and should be learned for future use. Penalties are given for a failure, indicating that the decision(s) made should not be retried and instead a new approach should be used.
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What is the machine's primary goal during the trial-and-error process in reinforcement learning?
Reinforcement learning models keep learning continuously, unlike supervised or unsupervised models.
In reinforcement learning, the machine aims to maximize the total _____ by using lessons from previous attempts.
Match each reinforcement learning term to its correct description.
Order the steps of applying reinforcement learning to a task like flying a helicopter.
Why is reinforcement learning considered more advanced than supervised or unsupervised learning?
Diagnose a poorly designed reward function for an autonomous helicopter landing task.
Why is it difficult to design a good reward function for a reinforcement learning task?
Why might a crashed helicopter trajectory receive a large negative reward like R(T) = -1,000?
The reward function for helicopter trajectories is generated automatically without human input.