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Robot Navigation Path Selection
An autonomous robot is being trained to navigate a warehouse. The robot's goal is to reach a charging station. It receives a reward of -1 for each second it takes to complete the task and a penalty of -50 if it collides with an obstacle. The robot completes two trial runs (episodes) with the following outcomes:
- Episode 1: The robot takes a cautious, longer route, taking 30 seconds to reach the station without any collisions.
- Episode 2: The robot attempts a shortcut, but collides with an obstacle once before reaching the station in 15 seconds.
Based on the objective of maximizing the cumulative sum of rewards, which episode represents a better outcome for the agent? Justify your answer by calculating the total reward for each episode.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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Application in Bloom's Taxonomy
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Baseline Method for Policy Gradient Variance Reduction
An agent is being trained in an environment where its sole objective is to maximize the sum of rewards it collects during an episode. The agent completes two separate episodes, receiving the following sequences of rewards:
- Episode A:
[+2, +2, +2, +2, +2] - Episode B:
[-5, -5, +10, +10, +1]
Based on the agent's primary objective, which statement correctly compares the outcomes of these two episodes?
- Episode A:
Robot Navigation Path Selection
Calculating Episode Return