bomberland
ViZDoom
| bomberland | ViZDoom | |
|---|---|---|
| 2 | 3 | |
| 99 | 1,667 | |
| - | 0.7% | |
| 3.1 | 8.9 | |
| 3 months ago | 3 months ago | |
| C++ | C++ | |
| MIT License | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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).
ViZDoom
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Reinforcement learning libraries with AlphaZero
AFAIK AlphaZero has not been used for continuous action space 3d environments like vizdoom, I wouldn't expect it to work well out of the box. There is a basic example demonstrating Q-learning on the environment: https://vizdoom.cs.put.edu.pl/tutorial#learning, as well as numerous example files of various training methods: https://github.com/Farama-Foundation/ViZDoom/tree/master/examples/python
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ViZDoom 1.2.0: Reinforcement Learning environments based on the 1993 game Doom
For more information about this release and ViZDoom, see https://github.com/Farama-Foundation/ViZDoom, and about Farama Foundation, see https://farama.org/, or join our Discord server: https://discord.gg/nhvKkYa6qX
- ViZDoom has joined the Farama Foundation
What are some alternatives?
q-learning-algorithms - This repository will aim to provide implementations of q-learning algorithms (DQN, Double-DQN, ...) using Pytorch.
envpool - C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
control-flag - A system to flag anomalous source code expressions by learning typical expressions from training data
open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
ultimate-volleyball - 3D RL Volleyball environment built on Unity ML-Agents
nodebuilder - An experimental DOOM Node Builder, written in C++
solve_the_spire - An AI to play the video game Slay the Spire
AI-Toolbox - A C++ framework for MDPs and POMDPs with Python bindings
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.
odamex - Odamex - Online Multiplayer Doom port with a strong focus on the original gameplay while providing a breadth of enhancements.