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PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning -- Distributed RL/IL code for Multi-Agent Path Finding (MAPF)

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PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning

Reinforcement learning code to train multiple agents to collaboratively plan their paths in a 2D grid world, as well as to test/visualize the learned policy on handcrafted scenarios.

NEW: Please try the brand new online interactive demo of our trained PRIMAL model! You can customize the grid size, add/remove obstacle, add agents and assign them goals, and finally run the model online and see the results.

File list

  • DRLMAPF_A3C_RNN.ipynb: Multi-agent training code. Training runs on GPU by default, change line "with tf.device("/gpu:0"):" to "with tf.device("/cpu:0"):" to train on CPU (much slower).
  • mapf_gym.py: Multi-agent path planning gym environment, in which agents learn collective path planning.
  • primal_testing.py: Code to run systematic validation tests of PRIMAL, pulled from the saved_environments folder as .npy files (examples available here) and output results in a given folder (by default: primal_results).
  • mapf_gym_cap.py: Multi-agent path planning gym environment, with capped goal distance state value for validation in larger environments.
  • mapgenerator.py: Script for creating custom environments and testing a trained model on them. As an example, the trained model used in our paper can be found here.

Before compilation: compile cpp_mstar code

  • cd into the od_mstar3 folder.
  • python3 setup.py build_ext (may need --inplace as extra argument).
  • copy so object from build/lib.*/ at the root of the od_mstar3 folder.
  • Check by going back to the root of the git folder, running python3 and "import cpp_mstar"

Custom testing

Edit mapgenerator.py to the correct path for the model. By default, the model is loaded from the model_primal folder.

Hotkeys:

  • o: obstacle mode
  • a: agent mod
  • g: goal mode, click an agent then click a free tile to place its goal
  • c: clear agents
  • r: reset
  • up/down arrows: change size
  • p: pause inference

Requirements

  • Python 3.4
  • Cython 0.28.4
  • OpenAI Gym 0.9.4
  • Tensorflow 1.3.1
  • Numpy 1.13.3
  • matplotlib
  • imageio (for GIFs creation)
  • tk
  • networkx (if using od_mstar.py and not the C++ version)

Authors

Guillaume Sartoretti

Justin Kerr

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PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning -- Distributed RL/IL code for Multi-Agent Path Finding (MAPF)

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