Implementation of Inverse Reinforcement Learning (IRL) algorithms in python/Tensorflow. Deep MaxEnt, MaxEnt, LPIRL
Implementation of selected Inverse Reinforcement Learning (IRL) algorithms in python/Tensorflow.
python demo.py
Please cite this work using the following bibtex if you use the software in your publications
@software{Lu_yrlu_irl-imitation_Implementation_of_2022,
author = {Lu, Yiren},
doi = {10.5281/zenodo.6796157},
month = {7},
title = {{yrlu/irl-imitation: Implementation of Inverse Reinforcement Learning (IRL) algorithms in python/Tensorflow}},
url = {https://github.com/yrlu/irl-imitation},
version = {1.0.0},
year = {2017}
}
$ python linear_irl_gridworld.py --act_random=0.3 --gamma=0.5 --l1=10 --r_max=10
(This implementation is largely influenced by Matthew Alger's maxent implementation)
$ python maxent_irl_gridworld.py --help
for options descriptions$ python maxent_irl_gridworld.py --height=10 --width=10 --gamma=0.8 --n_trajs=100 --l_traj=50 --no-rand_start --learning_rate=0.01 --n_iters=20
$ python maxent_irl_gridworld.py --gamma=0.8 --n_trajs=400 --l_traj=50 --rand_start --learning_rate=0.01 --n_iters=20
$ python deep_maxent_irl_gridworld.py --help
for options descriptions$ python deep_maxent_irl_gridworld.py --learning_rate=0.02 --n_trajs=200 --n_iters=20