Simple and easily configurable grid world environments for reinforcement learning
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Updated
Feb 6, 2025 - Python
Simple and easily configurable grid world environments for reinforcement learning
Lightweight multi-agent gridworld Gym environment
Accelerated minigrid environments with JAX
Simple Grid Environment for Gymnasium
MinRL provides clean, minimal implementations of fundamental reinforcement learning algorithms in a customizable GridWorld environment. The project focuses on educational clarity and implementation simplicity while maintaining production-quality code standards.
Easy MDPs and grid worlds with accessible transition dynamics to do exact calculations
Help! I'm lost in the flatland!
Tabular methods for reinforcement learning
OpenAI gym-based algorithm for the grid world problem
A simple Gridworld environment for Open AI gym
Old and new Reinforcement Learning algorithms run on the GridUniverse ecosystem
path planning using Q learning algorithm
Using value iteration to find the optimum policy in a grid world environment.
Deep Reinforcement Learning navigation of autonomous vehicles. Implementation of deep-Q learning, dyna-Q learning, Q-learning agents including SSMR(Skid-steering_mobile robot) Kinematics in various OpenAi gym environments
Implementation of Reinforcement Algorithms from scratch
Extended, multi-agent and multi-objective (MaMoRL / MoMaRL) environments based on DeepMind's AI Safety Gridworlds. This is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. It is made compatible with OpenAI's Gym/Gymnasium and Farama Foundation PettingZoo.
Simple Minimalistic Gridworld Environment for OpenAI Gym (Simple-MiniGrid)
This repository provides a simulation of 4-Room-World environment.
Implementations of model-based Inverse Reinforcement Learning (IRL) algorithms in python/Tensorflow. Deep MaxEnt, MaxEnt, LPIRL
Example Implementations of Reinforcement Learning Environments using Neodroid
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