Summary of Navigating Trade-offs: Policy Summarization For Multi-objective Reinforcement Learning, by Zuzanna Osika et al.
Navigating Trade-offs: Policy Summarization for Multi-Objective Reinforcement Learning
by Zuzanna Osika, Jazmin Zatarain-Salazar, Frans A. Oliehoek, Pradeep K. Murukannaiah
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 research paper, the authors employ Multi-objective reinforcement learning (MORL) to tackle complex problems involving multiple goals. The MORL agent must balance diverse signals from distinct reward functions to make informed decisions. By training an MORL agent, the researchers obtain a set of policies, each showcasing unique trade-offs among objectives. This approach improves explainability by allowing for fine-grained comparisons between policies based on their objective values rather than relying on a single policy. However, the resulting solution set is typically large and multi-dimensional, comprising policies represented by their objective values. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses Multi-objective reinforcement learning (MORL) to solve problems with multiple objectives. This means the agent has to make decisions based on different rewards. The researchers train an MORL agent and get a set of solutions (policies). Each policy shows how good or bad it is at balancing the different goals. MORL makes it easier to understand why some policies are better than others. But, the set of solutions can be very big and hard to work with. |
Keywords
* Artificial intelligence * Reinforcement learning
