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awesome-simulation

simulations for reinforcement learning
https://github.com/simjaecheol/awesome-simulation

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  • [PyBullet](https://github.com/bulletphysics/bullet3)

    • Guide
    • Github
    • pybullet-gym - Open-source implementation of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform
    • pybullet_robots - Prototyping robots for PyBullet
    • ravens - Train robotic agents to learn pick and place with deep learning for vision-based manipulation in PyBullet. Transporter Nets, CoRL 2020.
    • gym-pybullet-drones - PyBullet Gym environments for single and multi-agent reinforcement learning of quadcopter control
    • pybullet-planning - PyBullet Planning
    • pybullet_planning - A suite of utility functions to facilitate robotic planning related research on the pybullet physics simulation engine.
    • pybullet-robot-envs
    • pybullet_rendering - External rendering for PyBullet
    • quadruped_ctrl - MIT mini cheetah quadruped robot simulated in pybullet environment using ros.
    • panda-gym - OpenaAI Gym Franka Emika Panda robot environment based on PyBullet.
    • pybullet_multigoal_gym - Pybullet version of the multigoal robotics environment from OpenAI Gym
    • pybullet-robot-envs
  • [MuJoCo](https://mujoco.org)

    • dm_control - DeepMind's software stack for physics-based simulation
    • mujoco-py - mujoco-py allows using MuJoCo from Python 3
    • mujoco-worldgen - Automatic object XML generation for Mujoco
    • mjrl - Reinforcement learning algorithms for MuJoCo tasks
    • multiagent_mujoco - Benchmark for Continuous Multi Agent Robotic Control, based on OpenAI's Mujoco Gym environments
    • cassie-mujoco-sim - A simulation library for Agility Robotics' Cassie robot using MuJoCo
    • MuJoCo_RL_UR5 - 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.
    • Documents
    • Github
  • [NVIDIA-Omniverse](https://developer.nvidia.com/nvidia-omniverse-platform)

  • [Gazebo](http://gazebosim.org/)

  • [Brax](https://arxiv.org/abs/2106.13281)

  • [Unreal Engine](https://www.unrealengine.com/en-US/)

  • [Unity](https://unity.com/)

  • [Open AI Gym](https://gym.openai.com/)

    • Github - A toolkit for developing and comparing reinforcement learning algorithms.
  • [nvisii](https://nvisii.com/)

    • Github - A python-enabled ray tracing based renderer built on top of NVIDIA OptiX (C++/CUDA backend).