Summary of Conflict-averse Gradient Aggregation For Constrained Multi-objective Reinforcement Learning, by Dohyeong Kim et al.
Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning
by Dohyeong Kim, Mineui Hong, Jeongho Park, Songhwai Oh
First submitted to arxiv on: 1 Mar 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 constrained multi-objective reinforcement learning algorithm called Constrained Multi-Objective Gradient Aggregator (CoMOGA) to address complex real-world applications where agents need to consider multiple objectives while adhering to safety guidelines. The algorithm treats the maximization of multiple objectives as a constrained optimization problem, integrating existing safety constraints and updating the policy using a linear approximation to prevent gradient conflicts. CoMOGA guarantees optimal convergence in tabular settings and achieves constraint satisfaction across various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us learn about an AI system that can handle many goals at once while staying safe. It’s like having multiple bosses, but instead of doing what they say, the AI does what’s best for all of them. The system is designed to make sure it doesn’t get stuck in a bad situation and keeps making progress towards its goals. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
