TY - CONF A1 - Kister, Alexander A1 - Wrobel, S. A1 - Wirtz, T. A1 - Paul, N. T1 - Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs N2 - We interpret solving the multi-vehicle routing problem as a team Markov game with partially observable costs. For a given set of customers to serve, the playing agents (vehicles) have the common goal to determine the team-optimal agent routes with minimal total cost. Each agent thereby observes only its own cost. Our multi-agent reinforcement learning approach, the so-called multi-agent Neural Rewriter, builds on the single-agent Neural Rewriter to solve the problem by iteratively rewriting solutions. Parallel agent action execution and partial observability require new rewriting rules for the game. We propose the introduction of a so-called pool in the system which serves as a collection point for unvisited nodes. It enables agents to act simultaneously and exchange nodes in a conflict-free manner. We realize limited disclosure of agent-specific costs by only sharing them during learning. During inference, each agents acts decentrally, solely based on its own cost. First empirical results on small problem sizes demonstrate that we reach a performance close to the employed OR-Tools benchmark which operates in the perfect cost information setting. T2 - Gamification and Multiagent Solutions Workshop (ICLR 2022) CY - Online meeting DA - 29.04.2022 KW - Reinforcement learning KW - Deep Learning KW - Vehicle Routing PY - 2022 AN - OPUS4-56687 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Weichert, Dorina A1 - Kister, Alexander A1 - Volbach, Peter A1 - Houben, Sebastian A1 - Trost, Marcus A1 - Wrobel, Stefan T1 - Explainable production planning under partial observability in high-precision manufacturing N2 - Conceptually, high-precision manufacturing is a sequence of production and measurement steps, where both kinds of steps require to use non-deterministic models to represent production and measurement tolerances. This paper demonstrates how to effectively represent these manufacturing processes as Partially Observable Markov Decision Processes (POMDP) and derive an offline strategy with state-of-the-art Monte Carlo Tree Search (MCTS) approaches. In doing so, we face two challenges: a continuous observation space and explainability requirements from the side of the process engineers. As a result, we find that a tradeoff between the quantitative performance of the solution and its explainability is required. In a nutshell, the paper elucidates the entire process of explainable production planning: We design and validate a white-box simulation from expert knowledge, examine state-of-the-art POMDP solvers, and discuss our results from both the perspective of machine learning research and as an illustration for high-precision manufacturing practitioners. KW - Explainability KW - Manufacturing KW - Reinforcement Learning KW - Monte Carlo tree search KW - Partially observable Markov decision process PY - 2023 U6 - https://doi.org/10.1016/j.jmsy.2023.08.009 SN - 0278-6125 VL - 70 SP - 514 EP - 524 PB - Elsevier Ltd. CY - Southfield AN - OPUS4-58963 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -