The OffWorld Gym is undergoing maintenance and upgrades, and the real environment is inaccessible. For the time being, please take a look at some examples using our simulator.
The challenge that the community sets as a benchmark is usually the challenge that the community eventually solves. The ultimate challenge of reinforcement learning research is to train real agents to operate in the real environment, but until now there has not been a common real-world RL benchmark.
OffWorld Gym is free to use, try it out at gym.offworld.ai
We have created OffWorld Gym - a collection of real-world environments for reinforcement learning in robotics with free public remote access. Close integration into the existing ecosystem allows you to start using OffWorld Gym without any prior experience in robotics and removes the burden of managing a physical robotics system, abstracting it under a familiar API.
When testing your next RL algorithm on Atari, why not also gauge its applicability to the real world!
Install the library, change your gym.make('CartPole-v0') to gym.make('OffWorldMonolith-v0', ...) and you are all set to run your RL algorithm on a real robot, for free!
Environment 1: OffWorld Monolith
| Real | Description |
|---|---|
 OffWorldMonolithDiscreteReal-v0 |
Wheeled robot on an uneven terrain. Four discrete actions: left, right, forward, back. State space is RGB and/or Depth camera image. Sim version is available as OffWorldMonolithDiscreteSim-v0. |
 OffWorldMonolithContinousReal-v0 |
Wheeled robot on an uneven terrain. Two continuous actions: angular velocity, linear velocity. State space is RGB and/or Depth camera image. Sim version is available as OffWorldMonolithContinousSim-v0. |
 OffWorldMonolithObstacleDiscreteReal-v0 |
Wheeled robot on an uneven terrain with obstacles. Four discrete actions: left, right, forward, back. State space is RGB and/or Depth camera image. Sim version is available already now as OffWorldMonolithObstacleDiscreteSim-v0. |
 OffWorldMonolithObstacleContinousReal-v0 |
Wheeled robot on an uneven terrain with obstacles. Two continuous actions: angular velocity, linear velocity. State space is RGB and/or Depth camera image. Sim version is available already now as OffWorldMonolithObstacleContinousSim-v0. |
The main purpose of OffWorld Gym is to provide you with easy access to a physical robotic environment and allow you to train and test your algorithms on a real robotic system. To get access to the real robot, head to our web portal gym.offworld.ai and do the following:
- Register as a user at gym.offworld.ai.
- Book your experiment using the OffWorld Gym resource management system.
- Once you install the
offworld_gymlibrary, copy "OffWorld Gym Access Token" from your Profile page intoOFFWORLD_GYM_ACCESS_TOKENvariable in youroffworld-gym/scripts/gymshell.shscript.
The setup is complete! Now you can:
- Read about our mission in the About section.
- Browse the documentation, including the examples, at gym.offworld.ai/docs
- See the Leaderboard, can your algorithm do better?
- Run your experiments and monitor their performance under My Experiments
You can now install the offworld_gym library. Please follow the instructions in the Installation section of this documentation. Then proceed to the Examples.
Please check the Installation section of the documentation for the instructions.
There are minimal examples of how to connect to the real and sim robots and interact with them, please have a look at the Minimal example in the Real and Sim environment.
We also provide examples where an agent achieves learning in both the real and the simulated environment. We use PyTorch as Deep Learning Framework, alongside the Reinforcement Learning libraries such as Tianshou and Stable-baselines3. Our training scripts in examples/sim or examples/real allow you to make the training process resumable after an interruption. This is something that happens quite often when training in real.
This is something that happens quite often when training in real. The offworld_gym library itself does not depend on these tools - you can ignore them, build on top of them or use them for inspiration.
See the Examples section of the Docs for more details.