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cs.LG2024
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…
cs.LG2024
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…