solve_the_spire
bomberland
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| solve_the_spire | bomberland | |
|---|---|---|
| 1 | 2 | |
| 7 | 100 | |
| - | 2.0% | |
| 0.0 | 3.1 | |
| about 1 year ago | 3 months ago | |
| Julia | C++ | |
| - | MIT License |
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solve_the_spire
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Show HN: Bomberland – An AI competition to build the best Bomberman bot
https://github.com/DevJac/solve_the_spire
I stretched the truth a bit, I'm actually doing something like "hierarchical model-free reinforcement learning", even so, figuring out how to break the game down to create a hierarchy of agents is a lot of work. Basically, the AI is composed of about 8 different traditional RL agents (neural networks), each deciding a different thing. One chooses which cards to draft, one chooses which actions to take in combat, one chooses which path to take on the map, etc.
It shows definite signs of improvement, but has only reached a point where it can beat the act 1 boss about 50% of the time. I think that is its limit right now. I'm doing policy gradient which is very sample inefficient. I'm going to implement soft-actor-critic and see if it can do better with better sample efficiency.
And to reiterate my original point, I think each developer only has one or two reverse engineering attempts in them. I might otherwise be interested in this AI competition, but reverse engineering the environment to create my own model is just too daunting, I'm so burned out on it already.
bomberland
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Show HN: Bomberland – An AI competition to build the best Bomberman bot
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
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Bomberland: a new artificial intelligence competition
We have starter kits in Python and TypeScript to help you get started (and encourage any community contributions to the starter kit repo).
What are some alternatives?
ultimate-volleyball - 3D RL Volleyball environment built on Unity ML-Agents
q-learning-algorithms - This repository will aim to provide implementations of q-learning algorithms (DQN, Double-DQN, ...) using Pytorch.
bomberman - A bomberman game!
control-flag - A system to flag anomalous source code expressions by learning typical expressions from training data
iNeural - A library for creating Artificial Neural Networks, for use in Machine Learning and Deep Learning algorithms.
loneliless - A Deep-Q Network playing a single player Pong game. Network done in Python (Tensorflow-gpu) with the single player Pong game implemented in C++ (Openframeworks) and both binded with Pybind11.
ViZDoom - Reinforcement Learning environments based on the 1993 game Doom :godmode:
PythonMonkey - A Mozilla SpiderMonkey JavaScript engine embedded into the Python VM, using the Python engine to provide the JS host environment.
marching-squares - Marching squares (2D version of marching cubes) in C++