minihack
Minigrid
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| minihack | Minigrid | |
|---|---|---|
| 5 | 8 | |
| 449 | 2,008 | |
| 4.5% | 2.1% | |
| 6.8 | 6.9 | |
| 8 days ago | 4 days ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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minihack
- Difficult RL generalization benchmarks
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Anyone found any working replication repo for MuZero?
I have an implementation of Stochastic MuZero in JAX. It's been tested solely in MiniHack environments, but can be made to work in other environments by changing the representation function.
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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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Facebook AI Introduces ‘MiniHack’: A Sandbox Framework For Designing Rich And Diverse Environments For Reinforcement Learning (RL)
4 Min Read | Github |Paper
- MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
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?
gym-simplegrid - Simple Gridworld Gymnasium Environment
pytorch-blender - :sweat_drops: Seamless, distributed, real-time integration of Blender into PyTorch data pipelines
mctx - Monte Carlo tree search in JAX
MinAtar
gym-gridverse - Gridworld domains in the gym interface
rl-baselines-zoo - A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
ede - Code for the paper "Uncertainty-Driven Exploration for Generalization in Reinforcement Learning".
gym-super-mario-bros - An OpenAI Gym interface to Super Mario Bros. & Super Mario Bros. 2 (Lost Levels) on The NES
EfficientZero - Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.
ma-gym - A collection of multi agent environments based on OpenAI gym.
EfficientZero - Fork of EfficientZero to use newer libraries and to fix a few runtime bugs. Also includes pretrained models!
marlgrid - Gridworld for MARL experiments