Summary of Preference-conditioned Gradient Variations For Multi-objective Quality-diversity, by Hannah Janmohamed et al.
Preference-Conditioned Gradient Variations for Multi-Objective Quality-Diversity
by Hannah Janmohamed, Maxence Faldor, Thomas Pierrot, Antoine Cully
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 In this paper, researchers introduce a new Multi-Objective Quality-Diversity algorithm that uses preference-conditioned policy-gradient mutations and crowding mechanisms to efficiently discover promising regions of the objective space and promote a uniform distribution of solutions on the Pareto front. The proposed method, Multi-Objective Map-Elites with Preference-Conditioned Policy-Gradient and Crowding Mechanisms, outperforms or matches state-of-the-art methods in six robotics locomotion tasks, including two newly proposed tri-objective tasks. Additionally, it achieves a smoother set of trade-offs as measured by sparsity-based metrics, at a lower computational storage cost compared to previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates an algorithm that helps robots move better. It uses special tools like preference-conditioned policy-gradient mutations and crowding mechanisms to find the best ways for robots to move in different situations. The new algorithm does a better job than other algorithms in six tasks, making it useful for real-world applications. |
