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cs.LG2025
Heuristic Transformer: Belief Augmented In-Context Reinforcement Learning
Oliver Dippel, Alexei Lisitsa, Bei Peng
Transformers have demonstrated exceptional in-context learning (ICL) capabilities, enabling applications across natural language processing, computer vision, and sequential decisio…
cs.LG2025
MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures
Elena Zamaraeva, Christopher M. Collins, George R. Darling +8
Geometry optimization of atomic structures is a common and crucial task in computational chemistry and materials design. Following the learning to optimize paradigm, we propose a n…