Summary of Mitigating the Stability-plasticity Dilemma in Adaptive Train Scheduling with Curriculum-driven Continual Dqn Expansion, by Achref Jaziri et al.
Mitigating the Stability-Plasticity Dilemma in Adaptive Train Scheduling with Curriculum-Driven Continual DQN Expansion
by Achref Jaziri, Etienne Künzel, Visvanathan Ramesh
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 A novel approach to developing complex behaviors in agents is proposed in this paper. The agent learns from previous experiences and adapts to dynamic environments while preserving previously acquired knowledge. This process is critical for tackling real-world problems like the train scheduling problem, where environmental and agent behaviors are constantly changing, and the search space is vast. To address these challenges, a curriculum learning approach is employed, using adjacent skills that build on each other to improve generalization performance. A new algorithm, Continual Deep Q-Network (DQN) Expansion (CDE), is introduced to dynamically generate and adjust Q-function subspaces, handling environmental changes and task requirements. CDE mitigates catastrophic forgetting through EWC while ensuring high plasticity using adaptive rational activation functions. Experimental results show significant improvements in learning efficiency and adaptability compared to RL baselines and other adapted methods for continual learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers try to help computers learn from experience and adapt to changing situations. They want to make sure the computer remembers what it learned before while also learning new things. This is important because real-world problems like train scheduling require computers to work in dynamic environments where everything is always changing. To solve this problem, the researchers use a special way of teaching called curriculum learning. They design a series of tasks that build on each other, so the computer can learn and get better at each task. The researchers also introduce a new algorithm that helps the computer adapt to changes in its environment and forget less of what it learned before. |
Keywords
» Artificial intelligence » Continual learning » Curriculum learning » Generalization
