Learn Before
Return
Our final goal is to choose actions over time so that we could maximize the expected value of the return. The definition of return is as below: The return is the total discounted reward from time-step t. , where γ is the discounted factor. When γ close to 0 leads to “myopic” evaluation; γ close to 1 leads to “far-sighted” evaluation.
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Data Science
Foundations of Large Language Models Course
Computing Sciences
Learn After
An autonomous agent is being trained to navigate a grid. From its current position, it can choose one of two paths. Path A leads to an immediate reward of +10. Path B involves several steps with no immediate reward, but ultimately leads to a reward of +100. Two separate agents are trained for this task: Agent 1 uses a discount factor of 0.1, and Agent 2 uses a discount factor of 0.9. Based on these settings, which outcome is most likely?
Calculating Discounted Return
Selecting an Appropriate Discount Factor
Learn After
An autonomous agent is being trained to navigate a grid. From its current position, it can choose one of two paths. Path A leads to an immediate reward of +10. Path B involves several steps with no immediate reward, but ultimately leads to a reward of +100. Two separate agents are trained for this task: Agent 1 uses a discount factor of 0.1, and Agent 2 uses a discount factor of 0.9. Based on these settings, which outcome is most likely?
Calculating Discounted Return
Selecting an Appropriate Discount Factor