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AI Research
arXiv CS.AI·Jun 11·5 min read
Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions
TL;DR
In many scheduling problems, existing methods often yield suboptimal solutions, limiting efficiency. This research introduces an offline reinforcement learning approach that leverages random solutions to improve scheduling effectiveness.
✦ Why It Matters
Engineers can apply offline reinforcement learning techniques to improve scheduling efficiency in their projects.
Key Takeaways
- Implement CDQAC for effective scheduling with limited data.
- Focus on state-action coverage to enhance offline RL performance.
- Utilize random heuristics to generate diverse datasets.
- Evaluate scheduling solutions against both online and offline baselines.
How It Works
CDQAC employs a quantile-based critic to estimate the return distribution of actions taken in scheduling tasks. By using delayed policy updates, it effectively learns from static datasets, even if they are suboptimal, allowing it to generalize better across various scheduling scenarios.