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Stochastic Variance-Reduced Policy Gradient
Matteo Papini · Damiano Binaghi · Giuseppe Canonaco · Matteo Pirotta · Marcello Restelli

Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #65

In this paper, we propose a novel reinforcement-learning algorithm consisting in a stochastic variance-reduced version of policy gradient for solving Markov Decision Processes (MDPs). Stochastic variance-reduced gradient (SVRG) methods have proven to be very successful in supervised learning. However, their adaptation to policy gradient is not straightforward and needs to account for I) a non-concave objective function; II) approximations in the full gradient computation; and III) a non-stationary sampling process. The result is SVRPG, a stochastic variance-reduced policy gradient algorithm that leverages on importance weights to preserve the unbiasedness of the gradient estimate. Under standard assumptions on the MDP, we provide convergence guarantees for SVRPG with a convergence rate that is linear under increasing batch sizes. Finally, we suggest practical variants of SVRPG, and we empirically evaluate them on continuous MDPs.

Author Information

Matteo Papini (Politecnico di Milano)
Damiano Binaghi (Politecnico di Milano)
Giuseppe Canonaco (Politecnico di Milano)
Matteo Pirotta (SequeL - Inria Lille - Nord Europe)
Marcello Restelli (Politecnico di Milano)

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