The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)

Multi-Agent Connected Autonomous Driving (MACAD) Gym environments for Deep RL. Code for the paper presented in the Machine Learning for Autonomous Driving Workshop at NeurIPS 2019:

Grid2Op a testbed platform to model sequential decision making in power systems.

PyTorch implementation of Hierarchical Actor Critic (HAC) for OpenAI gym environments

Multi-objective Gymnasium environments for reinforcement learning

Collection of Reinforcement Learning / Meta Reinforcement Learning Environments.

Partially Observable Process Gym

Design Reinforcement Learning environments that model Active Network Management (ANM) tasks in electricity distribution networks.

A power network simulator with a Reinforcement Learning-focused usage.

Gym environments and agents for autonomous driving.

A collection of Gymnasium compatible games for reinforcement learning.

A toolkit for auto-generation of OpenAI Gym environments from RDDL description files.

An open-source framework to benchmark and assess safety specifications of Reinforcement Learning problems.

Pytorch Implementation of Stochastic MuZero for gym environment. This algorithm is capable of supporting a wide range of action and observation spaces, including both discrete and continuous variations.

Set of reinforcement learning environments for optical networks

Reinforcement learning in haskell

🎳 Environments for Reinforcement Learning

Cellular Automata Environments for Reinforcement Learning

Repository of implementation of few algorithms for Underactuated Systems in Robotics and solutions to some interesting problems

Framework for integrating ROS and Gazebo with gymnasium, streamlining the development and training of RL algorithms in realistic robot simulations.