PWIL
Primal Wasserstein Imitation Learning
**Primal 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.
Primal Wasserstein Imitation Learning
https://paperswithcode.com/paper/primal-wasserstein-imitation-learning
Primal Wasserstein Imitation Learning