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An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies
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An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies

Mirco Mutti
Marcello Restelli
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Papers citing "An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies"

6 / 6 papers shown
Title
Enhancing Diversity in Parallel Agents: A Maximum State Entropy Exploration Story
Enhancing Diversity in Parallel Agents: A Maximum State Entropy Exploration Story
Vincenzo De Paola
Riccardo Zamboni
Mirco Mutti
Marcello Restelli
125
0
0
02 May 2025
The Importance of Non-Markovianity in Maximum State Entropy Exploration
The Importance of Non-Markovianity in Maximum State Entropy Exploration
Mirco Mutti
Ric De Santi
Marcello Restelli
87
33
0
07 Feb 2022
Challenging Common Assumptions in Convex Reinforcement Learning
Challenging Common Assumptions in Convex Reinforcement Learning
Mirco Mutti
Ric De Santi
Piersilvio De Bartolomeis
Marcello Restelli
OffRL
94
23
0
03 Feb 2022
Action Redundancy in Reinforcement Learning
Action Redundancy in Reinforcement Learning
Nir Baram
Guy Tennenholtz
Shie Mannor
76
8
0
22 Feb 2021
Geometric Entropic Exploration
Geometric Entropic Exploration
Z. Guo
M. G. Azar
Alaa Saade
S. Thakoor
Bilal Piot
Bernardo Avila-Pires
Michal Valko
Thomas Mesnard
Tor Lattimore
Rémi Munos
97
32
0
06 Jan 2021
Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate
Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate
Mirco Mutti
Lorenzo Pratissoli
Marcello Restelli
75
19
0
09 Jul 2020