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Why is it difficult to design a good reward function for a reinforcement learning task?
Question: In one to three sentences, explain why designing a good reward function for a task like helicopter flight is not easy.
Sample answer: Designing a good reward function is difficult because it must balance multiple competing factors, such as how bumpy the landing was, whether the helicopter landed exactly at the desired spot, and how rough the ride was for passengers. Since the reward function is typically chosen by hand, capturing all of these trade-offs in a single score is challenging.
Key points:
- Reward function is typically chosen by hand
- Must trade off bumpiness, landing spot accuracy, and ride roughness
- Balancing multiple factors in one score makes design difficult
Rubric: Full credit: mentions that the reward function must trade off multiple factors (e.g., bumpiness, landing accuracy, ride roughness) and is chosen by hand. Partial credit: mentions only that it's hand-designed or only one trade-off factor.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Tags
Data Science
Foundations of Large Language Models Course
Computing Sciences
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Machine Learning Yearning @ DeepLearning.AI
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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.