Random Gym Environments

Gym environments with domain randomization (DR) support for sim-to-real research in robot learning. This repo uses the unmaintained version of gym, and the old mujoco bindings mujoco_py.

Features:

• Gym environments: cartpole, hopper, half-cheetah, walker2d, humanoid
• Noisy and unmodeled variants for each environment
• DR parametric distributions: uniform, normal, truncnormal
• Automatic sampling of new dynamics when env.reset() is called

Environments

	dim $\xi$	$\xi$	state noise
RandomCartPole-v0	4	Gravity, Cart mass, Pole mass & length	-
RandomHopper-v0	4	Link masses	-
RandomHopperNoisy-v0	4	Link masses	$10^{-4}$
RandomHopperUnmodeled-v0	3	Link masses	-
RandomHalfCheetah-v0	8	Link masses, friction	-
RandomHalfCheetahNoisy-v0	8	Link masses, friction	$10^{-4}$
RandomHalfCheetahUnmodeled-v0	5	Link masses, friction	-
RandomWalker2d-v0	13	Link masses and lengths, friction	-
RandomWalker2dNoisy-v0	13	Link masses and lengths, friction	$10^{-3}$
RandomWalker2dUnmodeled-v0	9	Link masses and lengths, friction	-
RandomHumanoid-v0	30	Link masses, joint damping	-
RandomHumanoidNoisy-v0	30	Link masses, joint damping	$10^{-3}$
RandomHumanoidUnmodeled-v0	23	Link masses, joint damping	-

where $\xi \in \mathbb{R}^{dim \ \xi}$ is the dynamics parameter vector. The unmodeled variants represent under-modeled parameterizations of the environments where dynamics parameters not included are misidentified by 20% (read more in Sec. 3.3 of our work).

Installation

##### Install mujoco 2.1 (or see https://github.com/openai/mujoco-py) #####
wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz 
mkdir ~/.mujoco
mv ~/mujoco210-linux-x86_64.tar.gz ~/.mujoco
cd ~/.mujoco
tar -xf mujoco210-linux-x86_64.tar.gz
# Install mujoco 2.1 dependencies through conda (sudo-free): https://github.com/openai/mujoco-py/issues/627
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/.mujoco/mujoco210/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia

##### Install repo requirements and repo #####
# git clone <this repo>
cd random-envs
pip install -r requirements.txt
pip install .

NOTE: you need to have the mujoco physics engine installed on your system as a prerequisite, as mentioned in the mujoco_py package.

Getting Started

import random_envs
import gym

env = gym.make('RandomHopper-v0')

env.set_dr_distribution(dr_type='uniform', distr=[0.9, 1.1, 1.9, 2.1, 2.9, 3.1, 3.9, 4.1])  # Randomize link masses uniformly
env.set_dr_training(True)

# ... train a policy

env.set_dr_training(False)

# ... evaluate policy in non-randomized env

See test.py for a pseudo-example in a sim-to-real transfer scenario. See train_random_envs.py in this repo for a full example of an actual training of an RL agent on random-envs environments.

Troubleshooting

• If having trouble while installing mujoco-py, see #627 to install all dependencies through conda.
• If installation goes wrong due to gym==0.21 as error in gym setup command: 'extras_require', see openai/gym#3176. There is a problem with the version of setuptools.
• if you get a cannot find -lGL error when importing mujoco_py for the first time (it could also be that it does it again on the cluster nodes), then have a look at my solution in #763
• if you get a fatal error: GL/osmesa.h: No such file or directory error, make sure you export the CPATH variable as mentioned in mujoco-py#627

Citing

If you use this repository, please consider citing

@misc{tiboniadrbenchmark,
    title={Online vs. Offline Adaptive Domain Randomization Benchmark},
    author={Tiboni, Gabriele and Arndt, Karol and Averta, Giuseppe and Kyrki, Ville and Tommasi, Tatiana},
    year={2022},
    primaryClass={cs.RO},
    publisher={arXiv},
    doi={10.48550/ARXIV.2206.14661}
}