5 papers
Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking
Alessandro Montenegro, Shihao Li, Puze Liu +2
Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate robustly, safely, and accurately…
Learning Deterministic Policies with Policy Gradients in Constrained Markov Decision Processes
Alessandro Montenegro, Leonardo Cesani, Marco Mussi +2
Constrained Reinforcement Learning (CRL) addresses sequential decision-making problems where agents are required to achieve goals by maximizing the expected return while meeting do…
Reusing Trajectories in Policy Gradients Enables Fast Convergence
Alessandro Montenegro, Federico Mansutti, Marco Mussi +2
Policy gradient (PG) methods are a class of effective reinforcement learning algorithms, particularly when dealing with continuous control problems. They rely on fresh on-policy da…
Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning
Alessandro Montenegro, Marco Mussi, Matteo Papini +1
Constrained Reinforcement Learning (CRL) tackles sequential decision-making problems where agents are required to achieve goals by maximizing the expected return while meeting doma…
Learning Optimal Deterministic Policies with Stochastic Policy Gradients
Alessandro Montenegro, Marco Mussi, Alberto Maria Metelli +1
Policy gradient (PG) methods are successful approaches to deal with continuous reinforcement learning (RL) problems. They learn stochastic parametric (hyper)policies by either expl…