Methods › Reinforcement Learning › Imitation Learning Methods › PWIL
Primal Wasserstein Imitation Learning
PWIL
Introduced by Robert Dadashi et al. in Primal Wasserstein Imitation Learning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
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.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Auto-Encoding Adversarial Imitation Learning 22 Jun 2022 · 0 repositories · arXiv:2206.11004
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Auto-Encoding Inverse Reinforcement Learning 29 Sep 2021 · 0 repositories
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Primal Wasserstein Imitation Learning 8 Jun 2020 · 2 repositories · arXiv:2006.04678Syntology ran 0 of 1 samples · 1 unverified
Tasks archive 2025-07-28
8 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Imitation Learning | 3 |
| Decision Making | 2 |
| MuJoCo | 2 |
| Reinforcement Learning (RL) | 2 |
| Continuous Control | 1 |
| Reinforcement Learning | 1 |
| continuous-control | 1 |
| reinforcement-learning | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections