Optical RL-Gym

OpenAI Gym is the de-facto interface for reinforcement learning environments. Optical RL-Gym builds on top of OpenAI Gym's interfaces to create a set of environments that model optical network problems such as resource management and reconfiguration. Optical RL-Gym can be used to quickly start experimenting with reinforcement learning in optical network problems. Later, you can use the pre-defined environments to create more specific environments for your particular use case.

Please use the following bibtex:

@inproceedings{optical-rl-gym,
  title = {The {Optical RL-Gym}: an open-source toolkit for applying reinforcement learning in optical networks},
  author = {Carlos Natalino and Paolo Monti},
  booktitle = {International Conference on Transparent Optical Networks (ICTON)},
  year = {2020},
  location = {Bari, Italy},
  month = {July},
  pages = {Mo.C1.1},
  doi = {10.1109/ICTON51198.2020.9203239},
  url = {https://github.com/carlosnatalino/optical-rl-gym}
}

Features

Across all the environments, the following features are available:

• Use of NetworkX for the topology graph representation, resource and path computation.
• Uniform and non-uniform traffic generation.
• Flag to let agents proactively reject requests or not.
• Appropriate random number generation with seed management providing reproducibility of results.

Content of this document

1. Installation
2. Environments
3. Examples
4. Resources
5. Contributors
6. Contact

Installation

You can install the Optical RL-Gym with:

git clone https://github.com/carlosnatalino/optical-rl-gym.git
cd optical-rl-gym
pip install -e .

You will be able to run the examples right away.

You can see the dependencies in the setup.py file.

To traing reinforcement learning agents, you must create or install reinforcement learning agents. Here are some of the libraries containing RL agents:

• Stable-baselines3
• TensorFlow Agents
• ChainerRL
• OpenAI Baselines -- in maintenance mode

Environments

At this moment, the following environments are ready for use:

1. RWAEnv
2. RMSAEnv
3. DeepRMSA

More environments will be added in the near future.

Examples

Training a RL agent for one of the Optical RL-Gym environments can be done with a few lines of code.

For instance, you can use a Stable Baselines agent trained for the RMSA environment:

# define the parameters of the RMSA environment
env_args = dict(topology=topology, seed=10, allow_rejection=False, 
                load=50, episode_length=50)
# create the environment
env = gym.make('RMSA-v0', **env_args)
# create the agent
agent = PPO2(MlpPolicy, env)
# run 10k learning timesteps
agent.learn(total_timesteps=10000)

We provide a set of examples.

Resources

• Introductory paper The Optical RL-Gym: an open-source toolkit for applying reinforcement learning in optical networks (paper and video to be published soon).
• List of publications using Optical RL-Gym
• How to implement your own algorithm

Contributors

Here is a list of people who have contributed to this project:

• Igor M. de Araújo [GitHub]
• Paolo Monti [Personal page]

Contact

This project is maintained by Carlos Natalino [Twitter], who can be contacted through carlos.natalino@chalmers.se.