Minigrid
marlgrid
| Minigrid | marlgrid | |
|---|---|---|
| 8 | 3 | |
| 2,010 | 133 | |
| 0.5% | - | |
| 6.9 | 0.0 | |
| 15 days ago | over 3 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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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
marlgrid
- What's the best way to approach a multi-agent learning application on a grid?
- RL environment for hard exploration (infinite) task
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Cooperative and competitive games for multi-agent deep reinforcement learning
I'm planning to start off with MARLGrid (https://github.com/kandouss/marlgrid) and then eventually to scale up to ML Agents in Unity3D (https://github.com/Unity-Technologies/ml-agents)
What are some alternatives?
pytorch-blender - :sweat_drops: Seamless, distributed, real-time integration of Blender into PyTorch data pipelines
ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
MinAtar
rl-baselines-zoo - A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
gym-super-mario-bros - An OpenAI Gym interface to Super Mario Bros. & Super Mario Bros. 2 (Lost Levels) on The NES
ma-gym - A collection of multi agent environments based on OpenAI gym.
rlcard - Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.
gym-simplegrid - Simple Gridworld Gymnasium Environment
modelicagym - Modelica models integration with Open AI Gym
minihack - MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
gym-gridverse - Gridworld domains in the gym interface
lab - A customisable 3D platform for agent-based AI research