Summary of Theoretical Study Of Conflict-avoidant Multi-objective Reinforcement Learning, by Yudan Wang et al.
Theoretical Study of Conflict-Avoidant Multi-Objective Reinforcement Learning
by Yudan Wang, Peiyao Xiao, Hao Ban, Kaiyi Ji, Shaofeng Zou
First submitted to arxiv on: 25 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 introduces a novel multi-task reinforcement learning (MTRL) algorithm, dynamic weighting multi-task actor-critic (MTAC), to address the issue of gradient conflict in existing MTRL methods. MTAC-CA and MTAC-FC are two sub-procedures that update task weights to optimize performance on multiple tasks. The authors provide finite-time convergence analysis for both algorithms and show that MTAC-CA can find a Pareto stationary policy with an +_{}-level CA distance using ({^{-5}}) samples, while MTAC-FC improves the sample complexity to (^{-3}). The authors also demonstrate the improved performance of their algorithms over existing MTRL methods with fixed preference on the MT10 benchmark. This paper contributes to the development of efficient and effective MTRL methods for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about a new way to teach machines to learn multiple tasks at once. The current methods have some problems, like one task getting too much attention and the others not doing well enough. The authors created a new algorithm called MTAC that tries to fix this issue by finding a good balance between all the tasks. They tested their algorithm on a benchmark test and showed that it works better than other methods. This research is important because it can help machines learn many things at once, which could be useful for things like self-driving cars or robots. |
Keywords
» Artificial intelligence » Attention » Multi task » Reinforcement learning
