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:

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

Collection of Reinforcement Learning / Meta Reinforcement Learning Environments.

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

Multi-objective Gymnasium environments for reinforcement learning

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.

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

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

Set of reinforcement learning environments for optical networks

Reinforcement learning in haskell

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.

🎳 Environments for Reinforcement Learning

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

Cellular Automata Environments for Reinforcement Learning

Implementations of a large collection of reinforcement learning algorithms.