Summary of Ucb-driven Utility Function Search For Multi-objective Reinforcement Learning, by Yucheng Shi et al.
UCB-driven Utility Function Search for Multi-objective Reinforcement Learning
by Yucheng Shi, Alexandros Agapitos, David Lynch, Giorgio Cruciata, Cengis Hasan, Hao Wang, Yayu Yao, Aleksandar Milenovic
First submitted to arxiv on: 1 May 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 paper proposes a method for Multi-objective Reinforcement Learning (MORL) that efficiently searches for the most promising weight vectors to optimize decision-making behaviors trading off between multiple objectives. The approach, based on Upper Confidence Bound, maximizes the hypervolume of the resulting Pareto front by decomposing the multi-objective problem into individual single-objective problems solved simultaneously. Experimental results show that this method outperforms various MORL baselines on Mujoco benchmark problems across different random seeds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, researchers are trying to create a better way for machines to learn and make decisions when there are multiple goals or rules to follow. They’re using a new method called Upper Confidence Bound to help the machine find the best combinations of these goals. This approach is useful because it allows the machine to optimize its performance in different situations. The results show that this method works well on specific benchmark tests. |
Keywords
» Artificial intelligence » Reinforcement learning
