Summary of Moduli: Unlocking Preference Generalization Via Diffusion Models For Offline Multi-objective Reinforcement Learning, by Yifu Yuan et al.
MODULI: Unlocking Preference Generalization via Diffusion Models for Offline Multi-Objective Reinforcement Learning
by Yifu Yuan, Zhenrui Zheng, Zibin Dong, Jianye Hao
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 MODULI (Multi-objective Diffusion Planner with Sliding Guidance), an offline Multi-objective Reinforcement Learning (MORL) algorithm that uses a preference-conditioned diffusion model as a planner. This approach addresses the issue of poor generalization to out-of-distribution (OOD) preferences in existing offline MORL algorithms. MODULI introduces two return normalization methods and a novel sliding guidance mechanism to capture direction changes in OOD preferences, allowing for patching and extending incomplete Pareto fronts. The algorithm is evaluated on the D4MORL benchmark, demonstrating excellent generalization to OOD preferences and outperforming state-of-the-art offline MORL baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MODULI is a new way to train artificial intelligence (AI) agents that can do multiple things at once. Currently, AI agents are not very good at this because they need to practice a lot online. The researchers came up with an idea called Offline Multi-objective Reinforcement Learning (MORL) that lets them train the agent offline using existing data. This is useful because it saves time and money. However, there’s still a problem – AI agents are not very good at handling new or unusual preferences. MODULI solves this by introducing a new way of guiding the agent to make better decisions based on different preferences. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Generalization » Reinforcement learning
