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The objective of my research is to build intelligent agents that discover and learn how to behave in the environment by interacting with it.

Repository Paper
choreographer Choreographer: Learning and Adapting Skills in Imagination [website] [arxiv]
mastering-urlb Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels [website] [arxiv]
contrastive-aif Contrastive Active Inference [website] [arxiv]
lbs-exploration Curiosity-Driven Exploration via Latent Bayesian Surprise [website] [arxiv]

Pietro Mazzaglia's Projects

choreographer icon choreographer

[ICLR 2023] Choreographer: a model-based agent that discovers and learns unsupervised skills in latent imagination, and it's able to efficiently coordinate and adapt the skills to solve downstream tasks.

contrastive-aif icon contrastive-aif

[NeurIPS 2021] Contrastive learning formulation of the active inference framework, for matching visual goal states.

lbs-exploration icon lbs-exploration

[AAAI-22] Curiosity-based objective for exploration with reinforcement learning in state-based and vision-based environments.

mastering-urlb icon mastering-urlb

[ICML 2023] Pre-train world model-based agents with different unsupervised strategies, fine-tune the agent's components selectively, and use planning (Dyna-MPC) during fine-tuning.

promp icon promp

ProMP: Proximal Meta-Policy Search

swishy icon swishy

Front-End Interface for a universitarian Start Up project

vime-pytorch icon vime-pytorch

PyTorch implementation of the VIME paper (Variational Information Maximizing Exploration)

vue-graph-vis icon vue-graph-vis

A Vue component to render graphs on canvas, based on Vis.js

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