Human-Aligned Decision Transformers for Circular Manufacturing
The article discusses the author's journey in developing human-aligned decision transformers for circular manufacturing supply chains, which face unique challenges like bidirectional material flows, quality uncertainty, and policy volatility.
Why it matters
This work highlights the importance of aligning AI systems with human values and constraints, especially in complex, real-world environments like circular manufacturing supply chains.
Key Points
- 1Reinforcement learning approaches struggled to handle the complexities of circular manufacturing supply chains
- 2Decision Transformers reframe RL as a sequence modeling problem, which can better handle variable-length sequences and multiple constraints
- 3The author adapted Decision Transformers to align AI decisions with human values and real-time policy constraints in circular manufacturing
Details
The author initially experimented with reinforcement learning to optimize a simple linear supply chain, but found that the most efficient solutions often violated real-world constraints and policies. This led the author to explore Decision Transformers, a framework that frames RL as a sequence modeling problem. However, the author found that Decision Transformers needed significant adaptation to handle the unique challenges of circular manufacturing supply chains, such as bidirectional material flows, quality uncertainty, and frequently changing policies. The key was aligning AI decisions with human values and constraints, rather than just optimizing for efficiency. The author's research journey involved studying recent papers on offline RL and human-in-the-loop systems to develop 'Human-Aligned Decision Transformers' specifically for circular manufacturing.
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