ma-gym
Minigrid
ma-gym | Minigrid | |
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2 | 8 | |
522 | 2,010 | |
- | 0.5% | |
6.1 | 6.9 | |
3 months ago | 12 days ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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ma-gym
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Tensorflow and OpenAI Gym for Multi-Agent Reinforcement Learning?
Of course I conducted research (or rather... searched on google :)) before making this post. I found a simple third party gym environment that implements multi-agent rl called 'ma-gym' (https://github.com/koulanurag/ma-gym) but I'm not quite sure how I would train two Agents and let them play inside one environment.
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How do you train Agent for something like Chess or Game of the Generals?
There are projects working on a multi agent version of gym. Maybe you find some inspiration there.
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?
mcts-general - General Python implementation of Monte Carlo Tree Search for the use with Open AI Gym environments.
pytorch-blender - :sweat_drops: Seamless, distributed, real-time integration of Blender into PyTorch data pipelines
crafter - Benchmarking the Spectrum of Agent Capabilities
MinAtar
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
rl-baselines-zoo - A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
GoBigger - [ICLR 2023] Come & try Decision-Intelligence version of "Agar"! Gobigger could also help you with multi-agent decision intelligence study.
gym-super-mario-bros - An OpenAI Gym interface to Super Mario Bros. & Super Mario Bros. 2 (Lost Levels) on The NES
rlcard - Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.
marlgrid - Gridworld for MARL experiments
gym-simplegrid - Simple Gridworld Gymnasium Environment