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.

翻译：本文旨在探讨在循环经济中运用强化学习方法进行生命周期评估的潜力，并提出该方向的一些新思路。为提供背景，我们阐释了强化学习在计算机国际象棋（及其他领域）的成功应用。鉴于计算机国际象棋历史上被称为"人工智能的果蝇"，我们首先描述了一种名为"旋转位棋盘"的棋盘表示方法，该方法在可持续发展背景下同样具有应用潜力。本文第一部分详细解释了位棋盘表示法的概念以及（旋转）位棋盘在走子生成中的优势。为具体说明这些理念，文中描述了FUSc#（数年前由柏林自由大学用C#开发的国际象棋引擎）中走子生成器的实际实现方式，并简要讨论了旋转二元神经网络。第二部分探讨计算机国际象棋（及其他领域）中的强化学习。通过对比2002-2008年开发FUSc#时的"技术前沿"与"AlphaZero"带来的突破性创新，我们例证了过去15-20年间该领域取得的进展。文中回顾了AlphaZero衍生理念在其他领域的应用案例，例如AlphaFold、AlphaTensor、AlphaGeometry和AlphaProof等"其他Alpha系列"成果。在最后部分，我们讨论了经济模式向（绝对）可持续发展转型所带来的计算机科学相关挑战，以及所谓"渐进式计算机科学"需要做出的贡献。具体挑战包括：在循环经济中通过生命周期评估实现材料闭环以优化（绝对）可持续性，我们针对这一方向提出了若干创新思路。