Kaixhin / Imitation Learning
Licence: mit
Imitation learning algorithms
Stars: ✭ 85
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IL
Imitation learning algorithms (with PPO [1]):
python main.py --imitation [AIRL|BC|DRIL|FAIRL|GAIL|GMMIL|RED]
Options include:
- State-only imitation learning:
--state-only - Absorbing state indicator [11]:
--absorbing - R1 gradient regularisation [12]:
--r1-reg-coeff 1(default)
Results
PPO
| Train | Test |
|---|---|
 |
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AIRL
| Train | Test |
|---|---|
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BC
| Train | Test |
|---|---|
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|
DRIL
| Train | Test |
|---|---|
 |
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FAIRL
| Train | Test |
|---|---|
 |
 |
GAIL
| Train | Test |
|---|---|
 |
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GMMIL
| Train | Test |
|---|---|
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RED
| Train | Test |
|---|---|
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Acknowledgements
References
[1] Proximal Policy Optimization Algorithms
[2] Adversarial Behavioral Cloning
[3] Learning Robust Rewards with Adversarial Inverse Reinforcement Learning
[4] Efficient Training of Artificial Neural Networks for Autonomous Navigation
[5] Disagreement-Regularized Imitation Learning
[6] A Divergence Minimization Perspective on Imitation Learning Methods
[7] Generative Adversarial Imitation Learning
[8] Imitation Learning via Kernel Mean Embedding
[9] Primal Wasserstein Imitation Learning
[10] Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
[11] Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
[12] Which Training Methods for GANs do actually Converge?
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