Summary of Demonstration Guided Multi-objective Reinforcement Learning, by Junlin Lu et al.
Demonstration Guided Multi-Objective Reinforcement Learning
by Junlin Lu, Patrick Mannion, Karl Mason
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Demonstration-Guided Multi-Objective Reinforcement Learning (DG-MORL), a novel approach to tackle the challenges of training policies from scratch in multi-objective reinforcement learning (MORL) scenarios. MORL is increasingly relevant due to its resemblance to real-world scenarios requiring trade-offs between multiple objectives. The proposed DG-MORL method utilizes prior demonstrations, aligns them with user preferences via corner weight support, and incorporates a self-evolving mechanism to refine suboptimal demonstrations. Empirical studies demonstrate the superiority of DG-MORL over existing MORL algorithms, establishing its robustness and efficacy, particularly under challenging conditions. The paper also provides an upper bound of the algorithm’s sample complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to train computers to make good choices when faced with multiple goals. This is like how humans make decisions in real life, where we have to balance different priorities. The researchers created a new method called DG-MORL that uses past examples of good behavior and adjusts them to fit what the user wants. They tested their method and found it was better than other methods at making good choices even when things got tough. This could be useful for things like self-driving cars or robots. |
Keywords
* Artificial intelligence * Reinforcement learning
