⛰ Reinforcement learning model trying to make car reach to top of mountain

Extended, multi-agent and multi-objective (MaMoRL) environments based on DeepMind's AI Safety Gridworlds. This is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. It is made compatible with OpenAI's Gym/Gymnasium and Farama Foundation PettingZoo.

Self hosted FLOSS fitness/workout, nutrition and weight tracker

Unreal environments for reinforcement learning

RLToolkit is a flexible and high-efficient reinforcement learning framework. Include implementation of DQN, AC,A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

An implementation of the "gridroboman" environment from the paper "Retrieval Augmented Reinforcement Learning".

World Model based Autonomous Driving Platform in CARLA 🚗

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.

An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)

Free Analytics for Strong Data.

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

Explore the capabilities of RealROS and MultiROS in training robots for real-world tasks. This repository showcases real-world training and Gazebo simulation-based training for a reach task based on the ReactorX 200 robot manipulator.

A grid-like environment (multi-agent system) used by an intelligent agent (or more than one agent) in order for it/them to carry the orbs to the pits in a limited number of movements.

RealROS is an open-source Python framework that seamlessly integrates with ROS (Robot Operating System) to create real-world robotics environments tailored for reinforcement learning (RL) applications. This modular framework simplifies RL development, enabling real-time training with physical robots

MultiROS is an open-source ROS based simulation environment designed for concurrent deep reinforcement learning. It provides a flexible and scalable framework for training and evaluating reinforcement learning agents for complex robotic tasks.

An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities

Multi-agent collaboration (2 UR10s) in Omniverse Isaac Gym/Sim.

Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.

PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control