HeteroMRTA

Code for RAL Paper: Heterogeneous Multi-robot Task Allocation and Scheduling via Reinforcement Learning.

This is a repository using deep reinforcement learning to address single-task agent (ST) multi-robot task(MR) task assignment problem. We train agents make decisions sequentially, and then they are able to choose task in a decentralized manner in execution.

Demo

Code structure

Three main structures of the code are as below:

1. Environments: generate random tasks locations/ requirements and agents with their depot.
2. Neural network: network based on attention in Pytorch
3. Ray framework: REINFORCE algorithm implementation in ray.

Running instructions

1. Set hyperparameters in parameters.py then run python driver.py

2. Testing the trained model by running python test.py

3. requirements:

   1. python => 3.6
   2. torch >= 1.8.1
   3. numpy, ray, matplotlib, scipy, pandas