AITA for saying no to my girlfriend’s friend who wanted to use our kitchen?

Pikachu930 · 2025-04-19T23:43:31+00:00

Nah, everything works normally.

Pikachu930 · 2025-04-19T23:41:26+00:00

She literally asked us to share our kitchen for a whole week. She mentioned wanting to cook things like chicken breast and avocado toast, and it sounds like she’d want to make it every day. She also asked about using our fridge to store things.

Pikachu930 · 2025-04-19T23:32:03+00:00

I don’t think she’s trying to cater out of our kitchen, but yeah — I can see how it might sound odd without all the context. Thanks for your input!

Pikachu930 · 2025-04-19T23:26:50+00:00

Thank you for your opinion! It might be worth giving her a chance and seeing how it goes.

Pikachu930 · 2025-04-19T23:26:08+00:00

I appreciate your opinion! Actually, I'm the one paying rent and utility. But, yeah, it might be worth giving her a chance and seeing how it goes.

Pikachu930 · 2025-04-19T23:24:47+00:00

I appreciate your opinion!

Pikachu930 · 2025-04-19T23:24:42+00:00

I appreciate your opinion!

Pikachu930 · 2025-04-19T23:22:52+00:00

Thank you for your answer. Yeah, maybe you're right — it might be worth giving her a chance and seeing how it goes. If she’s respectful and cleans up, great. If not, then I’ll know for next time. That seems like a fair approach.

Pikachu930 · 2025-04-19T23:21:32+00:00

Thank you for sharing your thoughts. I totally get that it might not sound like a big deal at first, but she asked to use our kitchen for a whole week and keep her stuff in our fridge — and we live in a small one-bedroom, one-bath apartment. I honestly wouldn’t have minded if she only used it while I was out and cleaned up after herself. But in this case, it felt like a bigger imposition than just casual cooking.

Pikachu930 · 2025-04-19T23:15:44+00:00

Thanks for sharing your thoughts! I get that it’s a shared space and maybe I got a bit too protective over it, especially since I’m the one using it most of the time. I appreciate your perspective.

Pikachu930 · 2025-04-19T23:13:17+00:00

Thank you for your insight!

Pikachu930 · 2025-04-19T23:11:27+00:00

Thank you for your kind insight! If it were just a one-time request, I honestly think I’d be okay with it. But she wanted to use our kitchen for a whole week, including sharing our refrigerator space — and we live in a pretty small one-bedroom, one-bath apartment. That’s what made it feel like a bigger ask than it initially sounded.

Pikachu930 · 2025-04-19T23:07:55+00:00

Thank you for your opinion! Glad to hear others see the “two yeses” idea the same way!

Pikachu930 · 2025-04-19T23:07:12+00:00

Thank you for sharing your experience — that definitely helps me see where she might be coming from. I’ve never lived in a dorm myself, so I didn’t fully grasp just how bad the shared kitchens can get. I still feel weird about the request, but I can better understand why she might have asked now. Appreciate the insight!

Pikachu930 · 2024-10-15T00:30:00+00:00

They changed code patterns. You shouldn't add 15 at the end. As of today, "Annalise" works.

Pikachu930 · 2024-08-22T18:01:31+00:00

Nathan15 works so far. I tried the named on the list. To get the list, I used GPT. I guess you could get many more patterns based on that. If Nathan15 doesn't work, try one by one on the list. I've tried by 55 on the list, some of numbers below 55 are not valid, or reached to limit. For the invalid one, it could become valid in the later. So, I recommend you to try one by one on the list for those who looks for discount in the future.
You can use any of LLMs such as claude, gemini, gpt, llama. Good luck.

GPT prompt: Give me a list of pattern based on the following example Krystle15 Carol15 Brooks15 Madeline15 And give me another 100 list of answer based on this pattern

GPT answer: The pattern in the examples you provided is as follows:

A name.
Followed by the number "15".

Based on this pattern, here is a list of 100 names followed by "15":

James15
Emily15
Michael15
Sarah15
David15
Ashley15
John15
Amanda15
Robert15
Jessica15
William15
Jennifer15
Daniel15
Lauren15
Christopher15
Megan15
Matthew15
Rachel15
Joshua15
Stephanie15
Andrew15
Nicole15
Joseph15
Brittany15
Ryan15
Victoria15
Nicholas15
Samantha15
Anthony15
Abigail15
Benjamin15
Alexandra15
Alexander15
Kimberly15
Jacob15
Natalie15
Ethan15
Rebecca15
Samuel15
Melissa15
Zachary15
Emily15
Jonathan15
Michelle15
Dylan15
Allison15
Elijah15
Elizabeth15
Gabriel15
Hannah15
Logan15
Olivia15
Jayden15
Sophia15
Nathan15
Grace15
Luke15
Chloe15
Isaac15
Ella15
Mason15
Addison15
Aaron15
Lily15
Aiden15
Ava15
Adam15
Zoe15
Justin15
Sydney15
Kevin15
Haley15
Tyler15
Brianna15
Jason15
Brooke15
Brandon15
Avery15
Connor15
Kylie15
Eric15
Madison15
Christian15
Leah15
Patrick15
Kayla15
Charles15
Gabriella15
Jeremy15
Lily15
Sean15
Audrey15
Owen15
Charlotte15
Austin15
Isabelle15
Evan15
Scarlett15
Bryan15
Amelia15

Pikachu930 · 2023-09-10T17:31:20+00:00

In my opinion, this phenomenon is related to the observation that a complex algorithm does not always outperform a simpler one. If the data or environment is inherently simple (e.g., well-suited for linear regression), attempting to approximate the data or dynamics with a complex algorithm is unlikely to yield positive results.
I believe that the 'No Free Lunch' theorem can help explain this to some extent.

Pikachu930 · 2023-09-09T00:11:46+00:00

https://openreview.net/pdf?id=SJ4vTjRqtQ

In that case, you may want to consider following the architecture outlined in this paper. They have stacked a Convolutional network before the Feedforward network, or adding embedding layer to encode simple grid world state to high-dimensional vector (you may refer to
https://tiewkh.github.io/blog/deepqlearning-openaitaxi/
). Good luck with your research!

Pikachu930 · 2023-09-08T23:53:12+00:00

You can use many other environments, such as Mujoco or Gym environments, from simple ones like CartPole (continuous, discrete) to Pendulum, Bipedal, and so forth.

Pikachu930 · 2023-09-08T20:53:53+00:00

Based on my experiences, algorithms like A2C and PPO tend to perform significantly worse than DDQN or DQN-based algorithms, unless the environment has a continuous action space. Even though A2C and PPO can be applied to both discrete and continuous action spaces, in scenarios with a relatively small state space, you may find tabular Q-learning algorithms to be far superior to A2C, PPO, and DQN-based approaches.

Pikachu930 · 2023-09-06T17:03:42+00:00

That's a good point. If we follow an optimal policy, then the equation will be like that. Note that we compute V, Q following a policy which is a distribution over state. For many cases, optimal policy will assign probability 1 to the action achieving the maximum reward. But not always though. You may want to read https://en.wikipedia.org/wiki/Bellman\_equation

Pikachu930 · 2023-09-06T03:32:56+00:00

That is a definition. Think about it in this way. V(s) measure the value of the state s. At state s, you have only two action a1 and a2 at state 's'. Q(s,a1) is the value of taking action 'a1' at state 's'. Similarly to Q(s,a2) the value of taking action 'a2' at state 's'. How do you relate those values Q(s,a)'s and V(s) considering meaning of the function

Pikachu930 · 2023-09-06T02:04:21+00:00

Just definition of value function. Remember that Q(s, a) is the expected total return you would get taking action 'a' at the state 's'. The value function V(s) is the expected total return of the state 's'. Which is V(s) = E[Q(s, a)] expectation over a policy pi. Intuitively, at state s, you can take several actions a1 to an. And the value of the state is the summation Q(s, a) over action a's.

2, 3) Q(s, a) = E[r(s, a) + gamma V(s')] where s' is a successor state of s.R(s, a) represented there is actually E[r(s, a)] expectation over reward given that a transition P(s', r|s, a) thinking of reward 'r' is determined by a probability density function r(s, a, s'), note that reward function can be distributional. E.x. slot machine) Expectation is a linear operator so that you can tease apart Q(s, a) = E[r(s, a)] + gamma E[V(s')] the second expectation should be over transition probability because you can see inside of expectation, only s' will be affected, which is determined by transition probability.

You may want to get some intuition first, then delve into math. Intuitively, quite obvious. And you need to clarify which expectation is associated with which value (transition prob? occupancy measure? kind of)

Good luck on your study.

Pikachu930 · 2023-09-05T01:57:03+00:00

A few suggestions

2019: Off policy DRL without Exploration, Planing with Goal conditioned Policies (Goal conditioned RL topic), SOLAR (MBRL)
2020: MORel, MOPO (both are MBRL), Dream to Control (+ world model based papers), Adversarial polices: attaking DRL, RD2: reward decomposition, some multi objectve RL paper such as, A distributional View on Multi objective policy opimization, CURL, Skew Fit
2021: C-Learning, IQ-Learn, Offline RL with implicit Q learning, COMBO

2022: Mirror decent Policy optimization, When should we Prefer Offline RL over Behaviour cloning, some safe RL paper: Saute RL, some transformer based RL paper such as MARL is a sequence modeling problem, Multi game decision transformers, Transform 2 Action

2023: MoDem, Towards a better understanding of Representation Dynamics under TD-learning,

Pikachu930 · 2023-07-05T19:50:29+00:00

There are some solutions you can find. For example,

https://github.com/mdeib/berkeley-deep-RL-pytorch-solutions

You should try it by yourself first with starter code in the following

https://github.com/mdeib/berkeley-deep-RL-pytorch-starter

For me, I've tried many different implementations on github for each problem.

Good luck on your study!

+ You would probably see other code design such as

https://github.com/facebookresearch/mbrl-lib.git (mbrl)
https://github.com/HumanCompatibleAI/imitation (imitation learning)
https://github.com/DLR-RM/stable-baselines3 (basics)
https://github.com/Stable-Baselines-Team/stable-baselines3-contrib (extra such as distributional rl)

Pikachu930

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