SIMULATION-BASED REINFORCEMENT LEARNING FOR SOCIAL DISTANCING
This is an open source pandemic simulation environment for reinforcement learning created with Unity ml-agents. This is a dissertation project for Advanced Computer Science Master in University of Sussex.
Open the "Project" folder in Pandemic_Simulation with Unity Editor. Play Assets/PandemicSimulation/Scenes/T3.unity
- This project uses mlagents Release 4 (It is migrated from Unity-ml-agent 1.1.0 Release 3)
- Unity 2919.4.0f1
- Anaconda Environment with Python 3.7
- Tensorflow 2.3.0
A step by step series of examples that tell you how to get a development env running
To train an agent:
- Go to the folder of config/PPO or config/SAC then open trainer_config.yaml (each algorithm has its own configuration file)
- Activate the environment
- Start Training
mlagents-learn is the main training utility provided by the ML-Agents Toolkit.
It accepts a number of CLI options in addition to a YAML configuration file that
contains all the configurations and hyperparameters to be used during training.
The set of configurations and hyperparameters to include in this file depend on
the agents in your environment and the specific training method you wish to
utilize. Keep in mind that the hyperparameter values can have a big impact on
the training performance (i.e. your agent's ability to learn a policy that
solves the task). In this page, we will review all the hyperparameters for all
training methods and provide guidelines and advice on their values.
To view a description of all the CLI options accepted by mlagents-learn, use
the --help:
mlagents-learn --helpThe basic command for training is:
mlagents-learn ./trainer_config.yaml --run-id first_run
To see the results use:
tensorboard --logdir results
This will open a localhost where you can see all the graphs that ml-agents provide to you
Single-Agent Environment: Surviving in an Epidemic outbreak and collecting rewards
Multi-Agent Environment: Social Distancing and discovery of self-quarantine
Different states of the agents
Through the simulation, the agent’s status of health changes. To represent the change, we used 4 different colors. a) White Bots indicates that the agent is not controlled by a brain. It only has simple hard-coded actions such as directly going targeted locations or bouncing from the walls. This represents individuals in a community who are not acting logically. b) Blue Bots are agents with a brain that controls them. c)Red Bots indicates whether with brain or not the bot is infected. d)Purple indicates that the agent is not infectious anymore. In SIR models’ purple agents call as Recovered-Removed.
- Ege Hosgungor - Initial work - Hsgngr
See also the list of contributors who participated in this project.
This project is inspired from followings:
If you find this useful for your research, please consider citing this Hsgngr. Please contact Ege Hosgungor <[email protected]> with any comments or feedback.