Summary of Conservative and Risk-aware Offline Multi-agent Reinforcement Learning, by Eslam Eldeeb et al.
Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning
by Eslam Eldeeb, Houssem Sifaou, Osvaldo Simeone, Mohammad Shehab, Hirley Alves
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA)
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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 new approach to multi-agent reinforcement learning (MARL) that can be used with offline data. Conventional MARL requires direct access to the physical environment during training, but this can be limiting. The proposed method integrates distributional RL and conservative Q-learning to address uncertainty in both the environment and the lack of exploration during training. This approach is applied to a trajectory planning problem in drone networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes an offline MARL scheme that helps control complex systems like next-generation wireless networks. When we only have data from the past, it’s hard to apply RL because we can’t directly interact with the environment. The new method combines two ideas: distributional RL and conservative Q-learning. This helps deal with uncertainty in both the environment and our lack of exploration during training. The approach is tested on drone networks for trajectory planning. |
Keywords
* Artificial intelligence * Reinforcement learning
