optimal-path-search_imitation-learning
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js-lee-AI
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
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[1] J. Ho, et al., "Generative Adversarial Imitation Learning", NIPS 2016.
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[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 / [email protected]
js-lee-AI