bomberland
q-learning-algorithms
| bomberland | q-learning-algorithms | |
|---|---|---|
| 2 | 1 | |
| 99 | 4 | |
| - | - | |
| 3.1 | 0.0 | |
| 3 months ago | almost 3 years ago | |
| C++ | Python | |
| MIT License | - |
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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).
q-learning-algorithms
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actor-critic algorithms
I learn quite some things about reinforcement learning in the last months, and I feel like I understand much better deep-Q learning algorithms (if you want, you can check my [repo](https://github.com/thomashirtz/q-learning-algorithms). I would like to change a little bit my focus towards actor-critics algorithms now. The only thing is, I feel like in comparison to Q-learning algorithms, the explanations of the papers are not as precise as for Q-learning, and explanations on the internet diverge really greatly (e.g. the original paper does not give the A2C but only the A3C for one learner).
What are some alternatives?
control-flag - A system to flag anomalous source code expressions by learning typical expressions from training data
chess - Program for playing chess in the console against AI or human opponents
ultimate-volleyball - 3D RL Volleyball environment built on Unity ML-Agents
AgileRL - Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
solve_the_spire - An AI to play the video game Slay the Spire
fragile - Framework for building algorithms based on FractalAI
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.
marching-squares - Marching squares (2D version of marching cubes) in C++
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.
bomberman - A bomberman game!