用强化学习优化循环经济的生命周期评估，探索可持续计算新路径。
Reinforcement Learning and Life Cycle Assessment for a Circular Economy -- Towards Progressive Computer Science
- 将棋类中的旋转位板技术迁移至可持续性建模，提升计算效率。
- 对比2002-2008与AlphaZero时代，展示强化学习在复杂决策中的飞跃。
- 面向绝对可持续性的计算科学挑战，提出新型算法思路，适合可持续计算研究者。
本文探讨将强化学习方法应用于循环经济中的生命周期评估潜力，并提出新思路。以计算机象棋为背景，类比人工智能的模式生物‘果蝇’，介绍‘旋转位板’的棋盘表示法及其在走法生成中的优势。通过描述柏林自由大学开发的FUSc#引擎中移动生成器的具体实现，说明该方法的实践价值。同时简要讨论旋转二进制神经网络。第二部分回顾过去15-20年强化学习在计算机象棋（及更广泛领域）的发展，对比2002-2008年与阿尔法零（AlphaZero）时代的进展，并列举阿尔法系列如AlphaFold、AlphaTensor、AlphaGeometry和AlphaProof的应用实例。最后，讨论向绝对可持续经济转型所引发的计算机科学挑战，强调‘进步型计算机科学’需在闭环材料循环与生命周期评估优化中发挥作用，并提出初步解决方案。
原文摘要 · Abstract (English)
The aim of this paper is to discuss the potential of using methods from Reinforcement Learning for Life Cycle Assessment in a circular economy, and to present some new ideas in this direction. To give some context, we explain how Reinforcement Learning was successfully applied in computer chess (and beyond). As computer chess was historically called the "drosophila of AI", we start by describing a method for the board representation called 'rotated bitboards' that can potentially also be applied in the context of sustainability. In the first part of this paper, the concepts of the bitboard-representation and the advantages of (rotated) bitboards in move generation are explained. In order to illustrate those ideas practice, the concrete implementation of the move-generator in FUSc# (a chess engine developed at FU Berlin in C# some years ago) is described. In addition, rotated binary neural networks are discussed briefly. The second part deals with reinforcement learning in computer chess (and beyond). We exemplify the progress that has been made in this field in the last 15-20 years by comparing the "state of the art" from 2002-2008, when FUSc# was developed, with the ground-breaking innovations connected to "AlphaZero". We review some application of the ideas developed in AlphaZero in other domains, e.g. the "other Alphas" like AlphaFold, AlphaTensor, AlphaGeometry and AlphaProof. In the final part of the paper, we discuss the computer-science related challenges that changing the economic paradigm towards (absolute) sustainability poses and in how far what we call 'progressive computer science' needs to contribute. Concrete challenges include the closing of material loops in a circular economy with Life Cycle Assessment in order to optimize for (absolute) sustainability, and we present some new ideas in this direction.
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