Hi, author here - that's a great point. When I first saw those results and how inaccurate they were, I thought there was a chance it was returning me an overfitted actual input image from training. Most likely not, but they were so realistic (and I was used to just seeing llamas until this point), that I thought I'd play it safe.
Hi, author here - I didn't specify that part, which is exactly why I love that image. The full prompt was "Action photo of a llama in a jersey dunking a basketball like Michael Jordan, dramatic backlighting, anime key visuals." (link to the image: https://labs.openai.com/s/5bVuPDdnv2O6xgxuleBlTZPj)
For now we'll be running weekly leaderboard matches, with the first one being for bots submitted by 11.59PM this Sunday UTC. After the matches, you'll be able to review your bot replays.
The longer-term goal would be to have an automated system where bots will play matches as soon as they've been submitted.
Prizes are still TBD - most likely will be a mix of cash prizes and/or merch. We're planning a proper tournament launch with prize pool and streams ETA ~Dec. You can think of this as an early-access playground to get familiar with the environment, and share any tooling needs/feedback/bugs before we officially launch and lock-in the major game rules.
Getting the platform to the point where people can spend most of the time on the actual training and experiments (and less on the infrastructure) is our current goal. We do have a forward model simulator which should let you step through the environment without re-implementing it, but if that's not what you're after, we'd love to chat more on what we could do to make this easier (feel free to ping any of us on Discord https://discord.gg/tRUMgdfC).
P.S. Sounds like a cool project! Have you heard of the Hearthstone AI competition (https://hearthstoneai.github.io/)? Might be of interest to you.
We usually encourage people to open-source their code after the competition so that the community improves over time (but only if they're open to it).
Speaking of Tensorflow, we're working on some ML starter kits and would love some feedback on how to improve the workflow for people using TF, PyTorch etc! If you do end up trying it out and get stuck anywhere, please feel free to ping either myself or Matt (@thegalah) on our Discord (https://discord.gg/tRUMgdfC).
No others (at the moment). We're a small team so Bomberland is our current focus - we want to improve the tooling first so that it's easy for people to dive into ML before we introduce other environments.
That is definitely a strategy :) Have seen people try imitation learning in other AI programming challenges too -- although usually they perform worse than the original agent they imitate.
Thanks for the feedback! We're working on improving the onboarding flow. Sorry about the Docker link issue - it should link you to a copy of the environment binary so that you can play around without Docker (no docs for this workflow just yet unfortunately).
It's essentially a Bomberman-inspired game, where you program the agents to play in it and can play against other users' agents. You can try it out without an account by cloning one of the starter kits here: https://github.com/CoderOneHQ/bomberland and following the usage instructions (but you'll need to create an account to use the visualizer and to submit agents).
We recommend the Docker flow, but if you get stuck feel free to reach out to me (Joy) or @thegalah (Matt) on our Discord: https://discord.gg/NkfgvRN
Speaking of Pommerman, we caught up recently with the organizer. While unfortunately Pommerman is no longer running, he was super helpful in giving us some advice for Bomberland.
It'd be awesome if there were previous participants of Pommerman here who could share some feedback on how we could improve Bomberland, since there are some obvious parallels.
Where CodinGame and TopCoder are great platforms for competitive programming, solvable programming problems and short-form competitions, our longer-term focus is more on open-ended, ongoing sandbox simulations that evolve over time. We think this format will lend itself more to challenging real-world simulations and ML approaches (think self-driving cars, drones, and challenging games like StarCraft II).
While Kaggle is great for classification-type problems and even recently started running their own simulation competitions, we feel there's a lot of room for a platform that is 100% purpose-built for simulation-type competitions (e.g. better visualisers, Twitch streams, matchmaking).
I'm Joy from Coder One. This is an early version of our upcoming Bomberman-inspired AI competition. Bomberland is an intentionally challenging environment for ML featuring non-trivial problems like real-time decision-making, large search space, and both adversarial + cooperative play.[1]
Longer term, we're building a place where anyone can explore cutting-edge algorithms like deep RL, GAN, MCTS etc on meaningful real-world challenges. Think OpenAI Gym, but with active competitions and open-ended multiplayer simulations.[2]
Also, I came across this article which suggests that at some point users were not allowed to share images generating human faces, artificial or not: https://mixed-news.com/en/openais-dall-e-2-may-now-generate-...