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InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations Chih-Hui Ho, Chun Hu, Po-Jung Lai 1 Outline 1. Introduction 2. Related work Generative adversarial imitation learning (GAIL) 3. Proposed method 4. Experiment

  1. InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations Chih-Hui Ho, Chun Hu, Po-Jung Lai 1

  2. Outline 1. Introduction 2. Related work ○ Generative adversarial imitation learning (GAIL) 3. Proposed method 4. Experiment results 5. Conclusion 2

  3. Introduction ● A reward function is important in RL task ● Hard to design reward function in some scenario (e.g. autonomous driving) ● Imitation learning allows agents to learn how to perform task like an expert ○ Generative Adversarial Imitation Learning (GAIL, [12]) ○ Generative adversarial nets (GANs, [13]) ● Expert demonstrations varies significantly ○ Multiple experts might have multiple policies ○ Need external latent factors to better represent the observed behavior ● Goal: To develop an imitation learning framework that is able to automatically discover and disentangle the latent factors of variation underlying expert demonstrations 3

  4. GAN for imitation learning (GAIL) https://www.youtube.com/watch?v=rOho-2oJFeA 4

  5. GAN for imitation learning (GAIL) 5

  6. Proposed method ● Introduce a latent factor c to represent the variation under expert demonstrations ● In GAIL, action is chosen as ● Proposed method chooses action as ● Maximize the mutual information between latent code c and {state, action}. ● is a function of GAIL InfoGAIL 6

  7. Proposed method ● Discriminator maximizes ● Mutual information minimizes ● Policy updates with TRPO[2] 7

  8. Proposed method ● Reward augmentation ○ Helps when expert perform sub-optimally ○ Hybrid between RL and imitation learning ● Replace vanilla GAN with WGAN[26] ○ More stable and easier to train ○ 8

  9. Experiment Result - Learning to Distinguish Trajectories ● The driving experiment are conducted on Open Source Race Car Simulator ● Each color denotes one specific latent code ○ Different experts have different trajectories 9

  10. Experiment Result - Interpretable Imitation Learning ● Blue and red indicate policies under different latent codes ● They correspond to “turning from inner lane” and “turning from outer lane” respectively 10

  11. Experiment Result - Interpretable Imitation Learning ● Different latent codes correspond to passing from right or left InfoGAIL GAIL 11

  12. Experiment 12

  13. Conclusion ● Automatically distinguish certain driving behaviors by introducing the latent factors ● Discovering the latent factors without direct supervision ● Perform imitation learning by using only visual inputs ● Learning a policy that can imitate and even outperform the human experts 13

  14. Demo Video 14

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