imitation-learning:模仿学习算法

A Pragmatic Look at Deep Imitation Learning =========================================== [](LICENSE.md) Imitation learning algorithms (with PPO [[1]](#references)): - ~~ABC [[2]](#references)~~ - AIRL [[3]](#references) - BC [[4]](#references) - DRIL [[5]](#references) - FAIRL [[6]](#references) - GAIL [[7]](#references) - GMMIL [[8]](#references) - nn-PUGAIL [[9]](#references) - ~~PWIL [[10]](#references)~~ - RED [[11]](#references) ``` python main.py --imitation [AIRL|BC|DRIL|FAIRL|GAIL|GMMIL|PUGAIL|RED] ``` Options include: - State-only imitation learning: `--state-only` - Absorbing state indicator [[12]](#references): `--absorbing` - R1 gradient regularisation [[13]](#references): `--r1-reg-coeff 1` (default) Results ------- **PPO** Train | Test :----:|:---:  |  **AIRL** Train | Test :----:|:---:  |  **BC** Train | Test :----:|:---:  |  **DRIL** Train | Test :----:|:---:  |  **FAIRL** Train | Test :----:|:---:  |  **GAIL** Train | Test :----:|:---:  |  **GMMIL** Train | Test :----:|:---:  |  **nn-PUGAIL** Train | Test :----:|:---:  |  **RED** Train | Test :----:|:---:  |  Acknowledgements ---------------- - [@ikostrikov](https://github.com/ikostrikov) for [https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail) Citation -------- If you find this work useful and would like to cite it, the following would be appropriate: ``` @misc{arulkumaran2020pragmatic, author = {Arulkumaran, Kai}, title = {A Pragmatic Look at Deep Imitation Learning}, url = {https://github.com/Kaixhin/imitation-learning}, year = {2020} } ``` References ---------- [1] [Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347) [2] [Adversarial Behavioral Cloning](https://www.tandfonline.com/doi/abs/10.1080/01691864.2020.1729237) [3] [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://arxiv.org/abs/1710.11248) [4] [Efficient Training of Artificial Neural Networks for Autonomous Navigation](https://www.mitpressjournals.org/doi/abs/10.1162/neco.1991.3.1.88?journalCode=neco) [5] [Disagreement-Regularized Imitation Learning](https://openreview.net/forum?id=rkgbYyHtwB) [6] [A Divergence Minimization Perspective on Imitation Learning Methods](https://arxiv.org/abs/1911.02256) [7] [Generative Adversarial Imitation Learning](https://arxiv.org/abs/1606.03476) [8] [Imitation Learning via Kernel Mean Embedding](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/16807) [9] [Positive-Unlabeled Reward Learning](https://arxiv.org/abs/1911.00459) [10] [Primal Wasserstein Imitation Learning](https://arxiv.org/abs/2006.04678) [11] [Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation](https://arxiv.org/abs/1905.06750) [12] [Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning](https://arxiv.org/abs/1809.02925) [13] [Which Training Methods for GANs do actually Converge?](https://arxiv.org/abs/1801.04406)