Summary of Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling, by Constantin Waubert De Puiseau et al.
Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling
by Constantin Waubert de Puiseau, Christian Dörpelkus, Jannik Peters, Hasan Tercan, Tobias Meisen
First submitted to arxiv on: 11 Jun 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 This research proposes innovative approaches to utilizing deep reinforcement learning (DRL) agents in scheduling problems, building upon recent advancements in learned construction heuristics. The study focuses on optimizing the use of trained DRL agents during inference, hypothesizing that agent behavior should be biased towards exploration or exploitation based on the acceptable computational budget. To achieve this, the authors introduce -sampling, a simple yet effective parameterization that manipulates the trained action vector to influence agent behavior. The proposed algorithm for obtaining the optimal parameterization is also presented. Experimental results validating the hypothesis and demonstrating improvements in generated solutions are included. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores new ways to use deep reinforcement learning (DRL) agents to solve scheduling problems. Right now, many people are working on making DRL agents better at solving these kinds of problems. But this research looks at how we can make the most out of the agents once they’re trained. It’s like deciding whether to explore a new area or stick with what you know when you’re trying to find a solution. The authors came up with a simple way to adjust the agent’s behavior based on how much time and resources are available. This helps the agent cover more ground while still finding good solutions. |
Keywords
» Artificial intelligence » Inference » Reinforcement learning
