reward design
reward design on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on reward design in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around reward design, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
[D] Looking for advice: Modelling a medicine-reminder agent that must decide “remind / wait / notify” under incomplete information[D]
Designing an AI agent for medicine reminders presents a compelling sequential decision challenge, particularly when dealing with incomplete patient information. Framing this as a POMDP might be an elegant theoretical approach, but simpler alternatives—like contextual bandits or MDPs with carefully engineered features—often prove more practical. Explore strategies for balancing timely reminders with alert fatigue, a common pitfall in these systems. For a deeper dive into AI agent capabilities, consider "Perplexity partners with Nvidia to launch Portable Computer," showcasing a fully local AI agent.
![Looking for feedback on my GPU-accelerated Snake AI project [P]](https://preview.redd.it/4k0bf6wgtneh1.gif?width=640&crop=smart&s=7309dc4cdba7df36b615ed9025f212c2b34fd4b0)
Looking for feedback on my GPU-accelerated Snake AI project [P]
Exciting progress in reinforcement learning! A developer has achieved an impressive average score of 86 (out of 87) in a GPU-accelerated Snake AI project after just 10 hours of training on a Google Colab T4. Leveraging a spatially-preserving CoordConv architecture, GPU-native simulation, and PPO + GAE, the system efficiently handles 4,096 concurrent Snake games. Seeking expert feedback on further optimization—particularly regarding exploration, reward design, or network architecture—the project invites contributions to enhance training efficiency. Explore the code and share insights on GitHub: [https://github.com/siddhartha399