computerender's profile picture.

Peter Whidden

@computerender

Šárka and Ondrěj have done incredible work building EbSynth, creating an entire image and video editor from scratch in webgl! Their attention to detail and the care they’ve taken at every aspect of its design is inspiring.

We're launching new EbSynth today 🎉 EbSynth is VFX software that lets you change your video by editing one frame. You can turn any video into your canvas, right in your browser. Go try it out 👇



talk is uploaded!

🦠Watch @computerender's first public talk on Mote: an interactive ecosystem simulation! Mote uses a custom GPU-based physics engine to model hundreds of thousands of organisms, leading to fascinating emergent phenomena: a sandbox that is part game, part research. Link below⬇️

recursecenter's tweet image. 🦠Watch @computerender's first public talk on Mote: an interactive ecosystem simulation!

Mote uses a custom GPU-based physics engine to model hundreds of thousands of organisms, leading to fascinating emergent phenomena: a sandbox that is part game, part research.

Link below⬇️


I’m giving a talk on my new project next month!

Join us on August 13 for Localhost! 🦠 @computerender will present Mote, an interactive ecosystem simulation with hundreds of thousands of organisms. His custom GPU physics engine models many simple behaviors at a massive scale, producing fascinating emergent phenomena. RSVP⬇️



Peter Whidden reposted

SENSEI can also guide exploration in combination with task rewards. When playing Pokémon Red from pixels, we achieve superior performance to Dreamer (pure task rewards) and Plan2Explore. Only SENSEI manages to obtain the first Gym Badge within 2M steps of exploration 🥇 7/8

CcansuSancaktar's tweet image. SENSEI can also guide exploration in combination with task rewards. When playing Pokémon Red from pixels, we achieve superior performance to Dreamer (pure task rewards) and Plan2Explore. Only SENSEI manages to obtain the first Gym Badge within 2M steps of exploration 🥇
7/8

Peter Whidden reposted

We trained a tiny model to beat Pokemon Red on 1 GPU. This is a qualitatively new result for RL because the game takes ~25 hours to beat! Most tasks used in research run seconds to minutes. Work with @dsrubinstein @DanAdvantage @kywch500 @computerender


David's been doing awesome work on this! It's been super fun to see this develop in the last couple years. Check out his blog post!

Excited to finally share our progress in developing a reinforcement learning system to beat Pokémon Red. Our system successfully completes the game using a policy under 10M parameters, PPO, and a few novel techniques. Blog posted below



The work Mykhailo has been doing on this project is incredibly impressive. He's created the first tensor/ml library that supports pytorch nn style code, realtime rendering, and non-nvidia gpus. He's already made a ton of amazing demos. Check out the blog post & star the repo!

I wrote a blog post about my own Python tensor compiler library called TensorFrost that I've been working on for the last 14 months! michaelmoroz.github.io/WritingAnOptim… As a sneak peak here is the last pet project that I've made using it - an implementation of Neural Cellular Automata:



Peter Whidden reposted

A YouTuber open sourced an RL algorithm for Pokemon Red and there's a multiplayer map where you can watch different people's models explore the game


Peter Whidden reposted

Yet another breakthrough: the AI has just managed to defeat the first gym leader!! 🏅🏅 progress has been accelerating recently, how long will it take to get to the next city? in-game playtime: 5h total training time: ~5,500h


Amazing to see how quickly a community is forming around RL for Pokemon. @dvruette and others have been working on support for gen3 games and are already making a ton of progress! More soon

Baby steps... AI has previously learned to play Pokemon Red, and now it's learning to play Pokemon Emerald as well. What will be the biggest obstacles on its journey to become a Pokemon master?



Looking forward to this!

Peter Whidden @computerender will be presenting his work on playing Pokemon with reinforcement learning at LIFE monthly Monday 11/13 at 9 AM PT / Noon ET. DM me your email for an invitation!



Peter Whidden reposted

Pokemon Red agent hacked our reward function. We gave points for gaining health, so it learned to repeatedly deposit and withdraw pokemon...oops Demo powered by Pufferlib. Project by @computerender. We're all in the Discord -- come hang out and contribute!


Over the last couple years I've been training a reinforcement learning agent to play pokemon red. I put together a video documenting the AI's learning, and my process creating it. Enjoy! 🔗 youtu.be/DcYLT37ImBY

computerender's tweet image. Over the last couple years I've been training a reinforcement learning agent to play pokemon red. I put together a video documenting the AI's learning, and my process creating it. Enjoy!
🔗 youtu.be/DcYLT37ImBY

Peter Whidden reposted

Nouvelle expérimentation d'installation interactive qui combine créativité humaine et intelligence artificielle. Créez votre composition avec des formes colorées et laissez l'IA vous surprendre avec son style unique.🎨🤖#AI #TouchDesigner #interactive vimeo.com/796622497

hello_im_flo's tweet card. Interactive Experiment #6 - AI Creative Partner

vimeo.com

Vimeo

Interactive Experiment #6 - AI Creative Partner


New tutorial available showing how to use computerender for vid2vid styling! github.com/computerender/… #stablediffusion #vid2vid


Peter Whidden reposted

Working on a component for TouchDesigner to seamlessly generate images with Stable Diffusion using computerender.com. I'm so excited to share the component and tutorial shortly. #stablediffusion #newmediaart #aiart #audioreactivevisuals


Loading...

Something went wrong.


Something went wrong.