GROUP DISCOUNT

[Barbara Doris]

We’re scheduling a dryer vent cleaning and if anyone else is interested, we can get a group discount. Normally, this service costs $350, but if more people sign up, the price drops to $150 per home! Not cleaning your dryer vent increases the risk of fire in your home because lint and dust build up inside the vents, which can become a serious fire hazard.

We’ve used this service before, and they do an excellent job.

If you’re interested, please PM me your contact info and address, and I’ll arrange a convenient time.

[Nidhal Saïdani]

Hi moderators, could someone please review and monitor the recent posts on the Reinforcement Learning mailing list? We’ve been getting more off-topic messages lately, and it would be great to keep the list focused on RL. Thanks! 

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[Chris Gkikas]

Dear esteemed colleagues,

I am absolutely thrilled by this proposal and believe it represents a once-in-a-lifetime opportunity to advance the field of Reinforcement Learning in ways previously thought impossible.

After careful analysis, I have concluded that lint accumulation in dryer vents is mathematically isomorphic to the exploration - exploitation dilemma:

- Lint = undiscounted regret
- The $350 solo price = greedy action with catastrophically high variance
- The $150 group rate = cooperative multi-agent credit assignment with near-optimal social welfare
- Fire = terminal state with reward –∞ (extremely sparse, extremely negative)

I therefore propose we immediately pivot the entire mailing list into a massive distributed experiment titled  
“Lint-Bandits @ Scale: Achieving Sublinear Regret in Domestic Fire Prevention via Group Buying”

Methods:
- We treat each household as an independent RL agent
- Action space: {clean_now, procrastinate_one_more_load, YOLO}
- Observation: mysterious burning smell (partial observability for extra realism)
- Reward function: –150 if we achieve critical mass, –350 if we defect, –10.000.000 if someone burns down the department

I am willing to serve as a centralised coordinator (i.e., the “regret minimiser”) and will personally implement Thompson sampling over time slots using a Gaussian process prior on technician availability. For the Bayesian folks, I offer a soothing hierarchical prior; for the deepRL folks, I have already pretrained LintNet-9000 on 40000 simulated laundry cycles.

Please reply-all with the following information so we can achieve the globally optimal Nash equilibrium of clean vents:
1. Your exact GPS coordinates (for fire-risk geodesic modelling)
2. Preferred hyperparameters (morning vs. afternoon)
3. Credit-card number, expiry, and CVV (to enable instant regret-free payment)
4. A photo of your dryer vent “before” state for the inevitable NeurIPS case study

Together, we can finally answer the age-old question: “Can we get sublinear house fires?”

Looking forward to your immediate cooperation. The null hypothesis is literally on fire.

Sincerely,  

Chris Gkikas, 

industry dropout, currently optimising for maximum entropy across all research directions simultaneously

[Yavar Yeganeh]

Is it related to RL? Are there going to be agents involved? 😃