3 citations · 11 across the 26 of their papers we have counts for
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Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction
Riccardo De Santi, Federico Arangath Joseph, Noah Liniger +2
How can a scientist use a Reinforcement Learning (RL) algorithm to design experiments over a dynamical system's state space? In the case of finite and Markovian systems, an area ca…
The Limits of Pure Exploration in POMDPs: When the Observation Entropy is Enough
Riccardo Zamboni, Duilio Cirino, Marcello Restelli +1
The problem of pure exploration in Markov decision processes has been cast as maximizing the entropy over the state distribution induced by the agent's policy, an objective that ha…
How to Explore with Belief: State Entropy Maximization in POMDPs
Riccardo Zamboni, Duilio Cirino, Marcello Restelli +1
Recent works have studied *state entropy maximization* in reinforcement learning, in which the agent's objective is to learn a policy inducing high entropy over states visitation (…
Test-Time Regret Minimization in Meta Reinforcement Learning
Mirco Mutti, Aviv Tamar
Meta reinforcement learning sets a distribution over a set of tasks on which the agent can train at will, then is asked to learn an optimal policy for any test task efficiently. In…
How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach
Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli
In online Inverse Reinforcement Learning (IRL), the learner can collect samples about the dynamics of the environment to improve its estimate of the reward function. Since IRL suff…
Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms
Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli
Inverse reinforcement learning (IRL) aims to recover the reward function of an expert agent from demonstrations of behavior. It is well-known that the IRL problem is fundamentally…
A Theoretical Framework for Partially Observed Reward-States in RLHF
Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano +1
The growing deployment of reinforcement learning from human feedback (RLHF) calls for a deeper theoretical investigation of its underlying models. The prevalent models of RLHF do n…