3 citations · 5 across the 5 of their papers we have counts for
6 papers · 1 filter
A Utility Maximization Model of Pedestrian and Driver Interactions
Yi-Shin Lin, Aravinda Ramakrishnan Srinivasan, Matteo Leonetti +2
Many models account for the traffic flow of road users but few take the details of local interactions into consideration and how they could deteriorate into safety-critical situati…
Information-theoretic Task Selection for Meta-Reinforcement Learning
Ricardo Luna Gutierrez, Matteo Leonetti
In Meta-Reinforcement Learning (meta-RL) an agent is trained on a set of tasks to prepare for and learn faster in new, unseen, but related tasks. The training tasks are usually han…
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
Sanmit Narvekar, Bei Peng, Matteo Leonetti +3
Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over th…
A gray-box approach for curriculum learning
Francesco Foglino, Matteo Leonetti, Simone Sagratella +1
Curriculum learning is often employed in deep reinforcement learning to let the agent progress more quickly towards better behaviors. Numerical methods for curriculum learning in t…
Curriculum Learning for Cumulative Return Maximization
Francesco Foglino, Christiano Coletto Christakou, Ricardo Luna Gutierrez +1
Curriculum learning has been successfully used in reinforcement learning to accelerate the learning process, through knowledge transfer between tasks of increasing complexity. Crit…
An Optimization Framework for Task Sequencing in Curriculum Learning
Francesco Foglino, Christiano Coletto Christakou, Matteo Leonetti
Curriculum learning in reinforcement learning is used to shape exploration by presenting the agent with increasingly complex tasks. The idea of curriculum learning has been largely…