dcss-ai-wrapper
maze
dcss-ai-wrapper | maze | |
---|---|---|
5 | 4 | |
38 | 258 | |
- | 1.2% | |
3.7 | 0.0 | |
3 months ago | 19 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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dcss-ai-wrapper
- Facebook AI which plays NetHack
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Publically Exposed/Accessible Crawl Data
Another very interesting project is the dcss-ai-wrapper. Which allows you to interface crawl from a program. You can use it to do reinforcement learning or write rule based bots.
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DCSS Python Bot (proof of concept)
Is your work related to https://github.com/dtdannen/dcss-ai-wrapper? There is a bit of academic work as well:
- Need help with what to focus my RL thesis on!
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Is there a consensus about RL frameworks?
DCSS AI Wrapper GitHub (under heavy development at the moment) https://github.com/dtdannen/dcss-ai-wrapper
maze
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[P] Maze: A Framework for Applied Reinforcement Learning
Check out Maze on GitHub - we'd love feedback from anybody with an interest and/or experience in reinforcement learning!
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Maze: A Framework for Applied Reinforcement Learning
Check out Maze on GitHub and its documentation here.
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Is there a consensus about RL frameworks?
For industrial and logistics problems this one looks promising: https://github.com/enlite-ai/maze saw their presentation 2 weeks ago at an international AI conference and was surprised that its already in use and available on github.
- MazeRL - Applied Reinforcement Learning with Python
What are some alternatives?
qw - The DCSS-playing bot qw
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
sequell - ##crawl bot Sequell; depends on https://github.com/crawl/go-sequell
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
nle - The NetHack Learning Environment
RL-Adventure - Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
dm_env - A Python interface for reinforcement learning environments
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.