Raw LLM Responses
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Imagine saying Musk has no moral compass. This just shows you how intelligent pe…
ytc_UgyiV5-DC…
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How do you people ever watch terminator judgment day? Autonomous Robots dont go …
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Here's a free lesson in art history:
'You're killing artists' careers'
Vincent …
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AI is finally giving corporate greed a way for the first time ever to realize th…
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Looks like they'll be "coping" for a long time since none of the hype claims abo…
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My technology teacher is telling us to use ai images for a “project”,we have to …
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My sister is an art student at ASU , there is some students at her school that c…
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Unpopular fact: Face recognition is a newer technology which has not been perfec…
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Comment
Typically, gamma is viewed as part of the problem, not of the algorithm. A reinforcement learning algorithm tries for each state to optimise the cumulative discounted reward:
r1 + gamma*r2 + gamma^2*r3 + gamma^3*r4 ...
where rn is the reward received at time step n from the current state. So, for one choice of gamma the algorithm may optimise one thing, and for another choice it will optimise something else.
However, when you have defined a certain high-level goal, there still often remains a modelling choice, as many different gamma's might satisfy the requirements of the goal.
In general, most algorithms learn faster when they don't have to look too far into the future. So, it sometimes helps the performance to set gamma relatively low. A general rule of thumb might be: determine the lowest gamma min_gamma that still satisfies your high-level goal, and then set the gamma to gamma = (min_gamma + 1)/2.
Hope that solves your query.
youtube
AI Governance
2020-10-20T11:4…
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | none |
| Reasoning | unclear |
| Policy | none |
| Emotion | indifference |
| Coded at | 2026-04-27T06:26:44.938723 |
Raw LLM Response
[
{"id":"ytr_Ugy6Z95H4v2LC9IASRV4AaABAg.9EYLk423JyI9Ep75vu312f","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgyJMpeIHjcqnIf6Y8h4AaABAg.9E8GhxSDC279F1boaf47QE","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgwPbs2bnV77PjCfhVd4AaABAg.9CTXRS-k8hy9CoQzbBKFcV","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgxG7kzon6DLRVupZgp4AaABAg.9BZRM0p718N9C5HvQ4GT3d","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgzHllJRZoEZOWi2lpF4AaABAg.9Ai2NdWCd4w9C5HgCvJBZE","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgzgODDBLXrwJ2fZ0hx4AaABAg.99-PLRlb7X59C5IFdJOXTm","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_Ugx6Kj_6y2_xnNW2dK94AaABAg.97TxpHm0YzE97hYkPCE3mm","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgxEmLvHkBgAbOV_D7l4AaABAg.90THB0z1tbM92U4VgjA9ds","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"},
{"id":"ytr_UgxnIjAVfQcdYHp72694AaABAg.9-nQirXxply93-nOhZOEa2","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgzymuNIIrFmE0ki5D54AaABAg.8zbUzxAfKXO8zwbXpqvP3W","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"}
]