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Roberto Capobianco

2 papers indexed

arxivcs.LGcs.AI2026-07-09

Open-ended Multi-agent Autocurricula via Visual Inspection of Policies with Multi-modal LLMs

Lorenzo Pantè, Andrea Fanti, Roberto Capobianco

Open-ended curricula in Reinforcement Learning (RL) aim to train generally-capable agents by identifying tasks that facilitate learning increasingly complex skills. A major challenge when designing such curricula is assessing task difficulty relative to the agent's current learni…

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arxivcs.AIcs.LG2026-07-01

Coachable agents for interactive gameplay

Roberto Capobianco, Harm van Seijen, Nolan D. Bard, Neil Burch, Fatima Davelouis, Josh Davidson, et al.

Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation models. Through trial-and-error, these AI systems typically learn one, near-optimal behavior to solve…

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