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Implementation of Inverse Reinforcement Learning (IRL) algorithms in Python/Tensorflow. Deep MaxEnt, MaxEnt, LPIRL
| Date | Stars |
|---|---|
| 2026-07-24 | 678 |
| 2026-07-25 | 678 |
| 2026-07-28 | 678 |
| 2026-07-30 | 678 |
| 2026-07-31 | 678 |
| 2026-08-06 | 678 |
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# irl-imitation
[](https://doi.org/10.5281/zenodo.6796157)
Implementation of selected Inverse Reinforcement Learning (IRL) algorithms in Python/Tensorflow.
```bash
$ python demo.py
```
<img src="imgs/cmp.jpg" width="830">
##### Implemented Algorithms
- Linear inverse reinforcement learning (Ng & Russell, 2000)
- Maximum entropy inverse reinforcement learning (Ziebart et al., 2008)
- Maximum entropy deep inverse reinforcement learning (Wulfmeier et al., 2015)
##### Implemented MDPs & Solver
- 2D gridworld
- 1D gridworld
- Value iteration
If you use this software in your publications, please cite it using the following BibTeX entry:
```bibtex
@misc{lu2017irl-imitation,
author = {Lu, Yiren},
doi = {10.5281/zenodo.6796157},
month = {7},
title = {{Implementations of inverse reinforcement learning algorithms in Python/Tensorflow}},
url = {https://github.com/yrlu/irl-imitation},
year = {2017}
}
```
#### Dependencies
- python 2.7
- cvxopt
- Tensorflow 0.12.1
- matplotlib
#### Linear Inverse Reinforcement Learning
- Following Ng & Russell 2000 paper: [Algorithms for Inverse Reinforcement Learning](http://ai.stanford.edu/~ang/papers/icml00-irl.pdf), algorithm 1
```bash
$ python linear_irl_gridworld.py --act_random=0.3 --gamma=0.5 --l1=10 --r_max=10
```
<img src="imgs/rmap_gt.jpg" width="200"> <img src="imgs/vmap_gt.jpg" width="200"> <img src="imgs/rmap_lirl.jpg" width="200"> <img src="imgs/rmap_lirl_3d.jpg" width="200">
#### Maximum Entropy Inverse Reinforcement Learning
(This implementation is largely influenced by [Matthew Alger's maxent implementation](https://github.com/MatthewJA/Inverse-Reinforcement-Learning/blob/master/irl/maxent.py))
- Following Ziebart et al. 2008 paper: [Maximum Entropy Inverse Reinforcement Learning](https://www.aaai.org/Papers/AAAI/2008/AAAI08-227.pdf)
- `$ python maxent_irl_gridworld.py --help` for options descriptions
<!-- ```
$ python maxent_gridworld.py --gamma=0.8 --n_trajs=100 --l_traj=20 --no-rand_start --learning_rate=0.01 --n_iters=20
```
<img src="imgs/rmap_gt_maxent.jpg" width="200"> <img src="imgs/vmap_gt_maxent.jpg" width="200"> <img src="imgs/rmap_maxent.jpg" width="200"> <img src="imgs/rmap_maxent_3d.jpg" width="200">
-->
```bash
$ 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
```
<img src="imgs/rmap_gt_maxent_10.jpg" width="200"> <img src="imgs/vmap_gt_maxent_10.jpg" width="200"> <img src="imgs/rmap_maxent_10.jpg" width="200"> <img src="imgs/rmap_maxent_3d_10.jpg" width="200">
```bash
$ python maxent_irl_gridworld.py --gamma=0.8 --n_trajs=400 --l_traj=50 --rand_start --learning_rate=0.01 --n_iters=20
```
<img src="imgs/maxent5_2r.jpg" width="830">
#### Maximum Entropy Deep Inverse Reinforcement Learning
- Following Wulfmeier et al. 2015 paper: [Maximum Entropy Deep Inverse Reinforcement Learning](https://arxiv.org/pdf/1507.04888.pdf). FC version implemented. The implementation does not follow exactly the model proposed in the paper. Some tweaks applied including elu activations, clipping gradients, l2 regularization etc.
- `$ python deep_maxent_irl_gridworld.py --help` for options descriptions
```bash
$ python deep_maxent_irl_gridworld.py --learning_rate=0.02 --n_trajs=200 --n_iters=20
```
<img src="imgs/deep_maxent_5s.jpg" width="830">
#### MIT License
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