lab
Minigrid
lab | Minigrid | |
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4 | 8 | |
7,002 | 2,008 | |
0.0% | 0.4% | |
0.0 | 6.9 | |
over 1 year ago | 8 days ago | |
C | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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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
Minigrid
- Environments that require long-term memory and reasoning
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Best GridWorld environment?
If you want something as simple as possible, I'd go with MiniGrid, and if you want to have a richer world with more complex settings, then MiniHack.
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Using FastAI to navigate matterport spaces?
This is a pretty hard domain to start with as someone "brand new" to AI. If you're interested in the vision aspect, I'd suggest you start by training a DNN for the CIFAR-10 task. There are plenty of tutorials out there. If you're more interested in the navigation aspect, you could start by training a Q-learning agent to solve some of the simpler problems in gym-minigrid.
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How to train an agent in custom mini-grid environment using stable baselines3?
Hello guys I tried to build a custom environment using maxicymeb repo
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What OpenAI Gym environments are your favourite for learning RL algorithms?
For learning and experimentation with RL algorithms, I suggest using a grid world implementation: observations are simple enough (most implementations have a one-hot layered observation) that you do not need deep conv layers to learn complex visual features. You can also make grid worlds as simple or as complex as you like by adding enemies, objects, key-door pairs, changing the size of the grid or decreasing observation radius, etc. There is a reason they are commonly used in research.
- RL environment for hard exploration (infinite) task
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[R] Are there any paper about reinforcement learning solving mazes?
Take a look at: https://github.com/maximecb/gym-minigrid
What are some alternatives?
ruby-fann - Ruby library for interfacing with FANN (Fast Artificial Neural Network)
pytorch-blender - :sweat_drops: Seamless, distributed, real-time integration of Blender into PyTorch data pipelines
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
MinAtar
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
rl-baselines-zoo - A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
gym - A toolkit for developing and comparing reinforcement learning algorithms.
gym-super-mario-bros - An OpenAI Gym interface to Super Mario Bros. & Super Mario Bros. 2 (Lost Levels) on The NES
CCV - C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library
ma-gym - A collection of multi agent environments based on OpenAI gym.
dm_memorytasks - A set of 13 diverse machine-learning tasks that require memory to solve.
marlgrid - Gridworld for MARL experiments