Repository navigation
gym-environment
- Website
- Wikipedia
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 <img alt="godmode" src="https://github.githubassets.com/images/icons/emoji/godmode.png?v8" />
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
Obstacle Tower Environment
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.
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
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 settings.