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cs.AI2026
CODE-SHARP: Continuous Open-ended Discovery and Evolution of Skills as Hierarchical Reward Programs
Richard Bornemann, Pierluigi Vito Amadori, Antoine Cully
A core quality of general intelligence is the ability to open-endedly expand and evolve its set of mastered skills autonomously. While recent Foundation Model (FM) driven approache…
cs.AI2025
Learning Representations in Video Game Agents with Supervised Contrastive Imitation Learning
Carlos Celemin, Joseph Brennan, Pierluigi Vito Amadori +1
This paper introduces a novel application of Supervised Contrastive Learning (SupCon) to Imitation Learning (IL), with a focus on learning more effective state representations for…