Summary of In Search For Architectures and Loss Functions in Multi-objective Reinforcement Learning, by Mikhail Terekhov et al.
In Search for Architectures and Loss Functions in Multi-Objective Reinforcement Learning
by Mikhail Terekhov, Caglar Gulcehre
First submitted to arxiv on: 23 Jul 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 This paper tackles the challenges of multi-objective reinforcement learning (MORL), a crucial aspect of real-world problems that require balancing multiple utility functions. To address this issue, researchers have focused on developing value-based loss functions. This study instead explores model-free policy learning loss functions and their impact on architectural choices. Two approaches are introduced: Multi-objective Proximal Policy Optimization (MOPPO) and Multi-objective Advantage Actor Critic (MOA2C). MOPPO is shown to effectively capture the Pareto front in MORL Deep Sea Treasure, Minecart, and Reacher environments. The study’s findings highlight the robustness and versatility of MOPPO compared to other popular MORL approaches like Pareto Conditioned Networks (PCN) and Envelope Q-learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to teach machines to make good decisions when there are multiple goals at play. It’s hard because these machines can get stuck or confused, but the researchers found a solution by trying different ways of teaching them. They created two new methods: MOPPO and MOA2C. These methods help machines learn to balance different goals and make better choices. The study tested these methods in different scenarios and showed that they work well. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
