Pietro Mazzaglia

genrl. [NeurIPS 2024] GenRL: Multimodal-foundation world models enable grounding language and video prompts into embodied domains, by turning them into sequences of latent world model states. Latent state sequences can be decoded using the decoder of the model, allowing visualization of the expected behavior, before training the agent to execute it.

choreographer. [ICLR 2023] Choreographer: a world-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.

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.

redundancy-action-spaces. [RA-L 2024] Novel action spaces leveraging redundancy in 7 DoF arms enable efficient & precise learning in robotic manipulation

contrastive-aif. [NeurIPS 2021] World modelling and action learning using a contrastive formulation of the active inference framework, for reaching visual goal states

vime-pytorch. PyTorch implementation of the VIME paper (Variational Information Maximizing Exploration)

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

vue-graph-vis. A Vue component to render graphs on canvas, based on Vis.js