Summary of Safe and Balanced: a Framework For Constrained Multi-objective Reinforcement Learning, by Shangding Gu et al.
Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learning
by Shangding Gu, Bilgehan Sel, Yuhao Ding, Lu Wang, Qingwei Lin, Alois Knoll, Ming Jin
First submitted to arxiv on: 26 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 novel framework for balancing multiple objectives while meeting stringent safety constraints in reinforcement learning (RL) problems involving safety-critical systems. The framework uses a primal-based approach to optimize policy between multi-objective learning and constraint adherence, employing a natural policy gradient manipulation method to overcome conflicting gradients. When hard constraints are violated, the algorithm rectifies the policy to minimize violations. Theoretical convergence and constraint violation guarantees are established in a tabular setting, and empirical results show that the proposed method outperforms prior state-of-the-art methods on challenging safe multi-objective RL tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a balance between different goals while keeping safety rules in mind when working with machines that learn from experience. The researchers created a new way to make decisions that considers many factors at once and makes sure the machine follows the safety rules. They tested their method on difficult problems and showed it works better than other methods. |
Keywords
» Artificial intelligence » Reinforcement learning
