Riccardo Zamboni

From Parameters to Behaviors: Unsupervised Compression of the Policy Space

Towards Principled Unsupervised Multi-Agent Reinforcement Learning

Enhancing Diversity In Parallel Agents: A Maximum State Entropy Exploration Story

No-regret Learning with Revealed Transitions in Adversarial Markov Decision Processes