gym-environment

Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.

Rainbow is all you need! A step-by-step tutorial from DQN to Rainbow

OpenAI Gym environments for an open-source quadruped robot (SpotMicro)

Scalable, event-driven, deep-learning-friendly backtesting library

Dynamics and Domain Randomized Gait Modulation with Bezier Curves for Sim-to-Real Legged Locomotion.

Gymnasium environments for NES Mario games, with task metadata, reward instrumentation, and reproducible evaluation interfaces.

Notes for the Reinforcement Learning course by David Silver along with implementation of various algorithms.

Texas holdem OpenAi gym poker environment with reinforcement learning based on keras-rl. Includes virtual rendering and montecarlo for equity calculation.

ns3-gym - The Playground for Reinforcement Learning in Networking Research

A MuJoCo/Gym environment for robot control using Reinforcement Learning. The task of agents in this environment is pixel-wise prediction of grasp success chances.

VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.

深度强化学习路径规划, SAC路径规划, Soft Actor-Critic算法, SAC-pytorch，激光雷达Lidar避障，激光雷达仿真，Adaptive-SAC

Framework for Multi-Agent Deep Reinforcement Learning in Poker

Obstacle Tower Environment

A general-purpose, flexible, and easy-to-use simulator alongside an OpenAI Gym trading environment for MetaTrader 5 trading platform (Approved by OpenAI Gym)

A simple, easy, customizable Gymnasium environment for trading.

Gym Electric Motor (GEM): An OpenAI Gym Environment for Electric Motors

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

Visually Realistic Underwater Robotics Simulator UNav-Sim

[ICLR-2025] POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF / MAPF settings.