Summary of Enabling Multi-agent Transfer Reinforcement Learning Via Scenario Independent Representation, by Ayesha Siddika Nipu et al.
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation
by Ayesha Siddika Nipu, Siming Liu, Anthony Harris
First submitted to arxiv on: 13 Feb 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 MARL algorithms are widely used for complex tasks that require collaboration and competition among agents in dynamic Multi-Agent Systems (MAS). However, learning such tasks from scratch can be arduous and may not always be feasible, particularly when dealing with large numbers of interactive agents. To address this issue, we introduce a novel framework that enables transfer learning for MARL by unifying various state spaces into fixed-size inputs. This allows a single unified deep-learning policy to learn from different scenarios within a MAS. Our evaluation in the StarCraft Multi-Agent Challenge (SMAC) environment shows significant enhancements in multi-agent learning performance when using maneuvering skills learned from other scenarios compared to agents learning from scratch. We also adopted Curriculum Transfer Learning (CTL), enabling our deep learning policy to progressively acquire knowledge and skills across pre-designed homogeneous learning scenarios organized by difficulty levels, promoting inter- and intra-agent knowledge transfer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MARL algorithms help teams of artificial intelligence agents work together in complex situations. However, making these teams learn new things from scratch can be difficult and time-consuming. To speed up this process, researchers have developed a way to let the agents learn from each other’s experiences. This approach allows the agents to adapt more quickly to changing situations and learn new skills faster. The study tested this method in various scenarios using a popular game-like environment called StarCraft, and the results showed that it significantly improved the agents’ ability to work together effectively. |
Keywords
* Artificial intelligence * Deep learning * Transfer learning
