Summary of Align Your Intents: Offline Imitation Learning Via Optimal Transport, by Maksim Bobrin et al.
Align Your Intents: Offline Imitation Learning via Optimal Transport
by Maksim Bobrin, Nazar Buzun, Dmitrii Krylov, Dmitry V. Dylov
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to Offline Reinforcement Learning (RL) called AILOT, which enables an imitating agent to learn optimal policy from observing expert behavior without explicit rewards or action labels. The method uses special state representations, incorporating pairwise spatial distances within the data, and defines an intrinsic reward function via optimal transport distance between expert and agent trajectories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way for AI agents to learn from experts without needing to interact with the environment or receive explicit rewards. This can be useful in situations where it’s hard to define what rewards are good or bad. The approach, called AILOT, uses special ways of representing states and defines its own reward function based on how similar expert and agent behaviors are. |
Keywords
* Artificial intelligence * Reinforcement learning
