A minimalist environment for decision-making in autonomous driving
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Updated
May 4, 2024 - Python
A minimalist environment for decision-making in autonomous driving
Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.
Scalable, event-driven, deep-learning-friendly backtesting library
Texas holdem OpenAi gym poker environment with reinforcement learning based on keras-rl. Includes virtual rendering and montecarlo for equity calculation.
Obstacle Tower Environment
OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
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
Gym Electric Motor (GEM): An OpenAI Gym Environment for Electric Motors
An OpenAI Gym environment for the Flappy Bird game
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.
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.
PyTorch implementation of Hierarchical Actor Critic (HAC) for OpenAI gym environments
A Gym-like environment for Reinforcement Learning in Rocket League
OpenAI Gym Environment API based Bitcoin trading environment
Lightweight multi-agent gridworld Gym environment
An OpenAI Gym environment wrapper for the Mupen64Plus N64 emulator
A python package for modelling locomotion in complex environments and spatially/velocity selective cell activity.
A large-scale benchmark for co-optimizing the design and control of soft robots, as seen in NeurIPS 2021.
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