Publications (10)
Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation
Carmelo Scribano, Elena Govi, Paolo Bertellini +3
Accurate mapping of agricultural field boundaries is essential for the efficient operation of agriculture. Automatic extraction from high-resolution satellite imagery, supported by…
Model-Based Exploration in Monitored Markov Decision Processes
Alireza Kazemipour, Simone Parisi, Matthew E. Taylor +1
A tenet of reinforcement learning is that the agent always observes rewards. However, this is not true in many realistic settings, e.g., a human observer may not always be availabl…
Interesting Object, Curious Agent: Learning Task-Agnostic Exploration
Simone Parisi, Victoria Dean, Deepak Pathak +1
Common approaches for task-agnostic exploration learn tabula-rasa --the agent assumes isolated environments and no prior knowledge or experience. However, in the real world, agents…
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control
Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam +1
Recent years have seen the emergence of pre-trained representations as a powerful abstraction for AI applications in computer vision, natural language, and speech. However, policy…
Multi-objective Reinforcement Learning with Continuous Pareto Frontier Approximation Supplementary Material
Matteo Pirotta, Simone Parisi, Marcello Restelli
This document contains supplementary material for the paper "Multi-objective Reinforcement Learning with Continuous Pareto Frontier Approximation", published at the Twenty-Ninth AA…
Beyond Optimism: Exploration With Partially Observable Rewards
Simone Parisi, Alireza Kazemipour, Michael Bowling
Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent le…