Summary of Pareto Inverse Reinforcement Learning For Diverse Expert Policy Generation, by Woo Kyung Kim and Minjong Yoo and Honguk Woo
Pareto Inverse Reinforcement Learning for Diverse Expert Policy Generation
by Woo Kyung Kim, Minjong Yoo, Honguk Woo
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed Pareto Inverse Reinforcement Learning (ParIRL) framework addresses sequential decision-making problems by learning Pareto-optimal policies from a limited pool of expert datasets. By adapting inverse reinforcement learning using reward distance estimates, the discriminator is regularized to generate a set of policies accommodating diverse preferences on multiple objectives. The framework uses only two distinct datasets, each associated with a different expert preference, and distills the Pareto policy set into a single, preference-conditioned diffusion model. This allows users to immediately specify which expert’s patterns they prefer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ParIRL is a new approach for learning Pareto-optimal policies from limited expert data. It uses inverse reinforcement learning with reward distance estimates to generate a set of policies that fit different preferences on multiple objectives. The framework works by using only two datasets, one for each expert preference, and then distilling the results into a single model that users can customize. |
Keywords
» Artificial intelligence » Diffusion model » Reinforcement learning
