mlsanityData to 14 Oct 2026

Reinforcement Learning and Life Cycle Assessment for a Circular Economy -- Towards Progressive Computer Science

arXiv 2503.10822 ↗First posted 13 Mar 2025cs.AI cs.CYcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 26 references that are in the map. First posted on arXiv 13 Mar 2025.

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2 of 26 references.

References · 2

  1. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
  2. 2019Forward and Backward Information Retention for Accurate Binary Neural Networkscited by 169

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  7. 2024Model Predictive Control and Reinforcement Learning: A Unified Framework Based on Dynamic Programming
  8. 2018A0C: Alpha Zero in Continuous Action Space