gym-environment

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

A minimalist environment for decision-making in autonomous driving

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

Reinforcement Learning environments based on the 1993 game Doom

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.

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

Build, evaluate and train General Multi-Agent Assistance with ease

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.

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)

Framework for Multi-Agent Deep Reinforcement Learning in Poker

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.

A simple, easy, customizable Gymnasium environment for trading.

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

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

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