js-lee-AI / optimal-path-search_imitation-learning

Searching for optimal paths in a customized Grid-world environment using Imitation Learning; Variational Adversarial Imitation Learning [VAIL]

Imitation Learning; Optimal Multiple Path Search Using VAIL

How to

The Customized Grid-World environment and actions

environment.py : Currently, the customized Grid-World of the 20x20 pixel window is configured.

Expert dataset 1,2 : Examples of configuring expert dataset with the pickle module

expert_generator.py : You can use this file to create expert data.

main.py : You can run this program by running main.py.

Result

two obstacles - 10 x 10 GridWorld

You should need expert data to find approximately 50 shortest paths.

This is a captured image executed from our old code.

150 episode

500 episode

four obstacles - easy path

You should need expert data to find approximately 200 shortest paths.

300 episode

500 episode

700 episode

900 episode

1000 episode

four obstacles - difficult path

You should need expert data to find approximately 400-500 shortest paths.

700 episode

900 episode

1000 episode

Related papers

• [1] J. Ho, et al., "Generative Adversarial Imitation Learning", NIPS 2016.

• [2] Xue Bin Peng, et al., "Variational Discriminator Bottleneck. Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow", ICLR 2019.

Reference

RL-korea : Dongmin Lee, et al.

Author

Jungseob Lee / js-lee-AI / [e-mail]