OpenNARS-for-Applications
lab
OpenNARS-for-Applications | lab | |
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1 | 4 | |
86 | 7,002 | |
- | 0.0% | |
7.5 | 0.0 | |
27 days ago | over 1 year ago | |
C | C | |
MIT License | GNU General Public License v3.0 or later |
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OpenNARS-for-Applications
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From here to proto-AGI: what might it take and what might happen
NARS ( https://github.com/opennars/OpenNARS-for-Applications )
lab
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:
- Environments that require long-term memory and reasoning
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Keys doors puzzle in dmlab30
dmlab30 is a test suite of 30 environments for Deep RL research, maintained by DeepMind. https://github.com/deepmind/lab/tree/master/game_scripts/levels/contributed/dmlab30#readme
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[R] Would DeepMind Control Suite be enough for a publication?
DeepMind Lab is also interesting: https://deepmind.com/blog/article/open-sourcing-deepmind-lab https://github.com/deepmind/lab
What are some alternatives?
opennars - OpenNARS for Research 3.0+
ruby-fann - Ruby library for interfacing with FANN (Fast Artificial Neural Network)
phoebe - Phoebe
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
Genann - simple neural network library in ANSI C
PHP IDS - PHPIDS (PHP-Intrusion Detection System) is a simple to use, well structured, fast and state-of-the-art security layer for your PHP based web application
MV-Tractus - A simple tool to extract motion vectors from h264 encoded videos.
gym - A toolkit for developing and comparing reinforcement learning algorithms.
CCV - C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library
dm_memorytasks - A set of 13 diverse machine-learning tasks that require memory to solve.
PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities