dcss-ai-wrapper
RL-Adventure
| dcss-ai-wrapper | RL-Adventure | |
|---|---|---|
| 5 | 3 | |
| 38 | 2,950 | |
| - | - | |
| 3.7 | 0.0 | |
| 3 months ago | over 2 years ago | |
| Python | Jupyter Notebook | |
| MIT License | - |
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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
RL-Adventure
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Applied resources in Pytorch?
can check this out.
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Is there a consensus about RL frameworks?
For me, nothing beats RL-Adventure. It's not perfect, it has bugs, but it's super readable and will let you level up through the different main methods.
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[N] 20 hours of new lectures on Deep Learning and Reinforcement Learning with lots of examples
Lecture 7: Function approximation. (slides, code, video)
What are some alternatives?
qw - The DCSS-playing bot qw
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
sequell - ##crawl bot Sequell; depends on https://github.com/crawl/go-sequell
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
nle - The NetHack Learning Environment
maze - Maze Applied Reinforcement Learning Framework
RL-Adventure-2 - Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters [Moved to: https://github.com/higgsfield-ai/higgsfield]
flux-beamer - Flux is a modern style beamer presentation.
acme - A library of reinforcement learning components and agents