Imitation Learning Methods
Primal Wasserstein Imitation Learning
Introduced by Dadashi et al. in Primal Wasserstein Imitation LearningPrimal Wasserstein Imitation Learning, or PWIL, is a method for imitation learning which ties to the primal form of the Wasserstein distance between the expert and the agent state-action distributions. The reward function is derived offline, as opposed to recent adversarial IL algorithms that learn a reward function through interactions with the environment, and requires little fine-tuning.
Source: Primal Wasserstein Imitation Learning
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Imitation Learning | 3 | 37.50% |
| Decision Making | 2 | 25.00% |
| Reinforcement Learning (RL) | 2 | 25.00% |
| Continuous Control | 1 | 12.50% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
