Summary of Imitation Game: a Model-based and Imitation Learning Deep Reinforcement Learning Hybrid, by Eric Msp Veith et al.
Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybrid
by Eric MSP Veith, Torben Logemann, Aleksandr Berezin, Arlena Wellßow, Stephan Balduin
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This study addresses two key challenges in using Deep Reinforcement Learning for Cyber-Physical Energy Systems: requiring large sample sizes and struggling with concept drifts. The authors propose a hybrid approach combining model-based DRL and imitation learning to improve policy learning and resilience. By leveraging model-free algorithms like Soft Actor Critic, the proposed architecture aims to reduce sample requirements while mitigating catastrophic forgetting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers are working on a new way to make energy systems smarter using AI. They’re trying to fix two big problems: needing lots of data to learn and getting stuck when things change suddenly. The solution is a special kind of AI that combines two types of learning: one that uses models and one that learns from examples. This will help create more efficient and resilient energy systems. |
Keywords
» Artificial intelligence » Reinforcement learning
