Timezone: »

 
Poster
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 Posters Thu »

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.

[ PDF] 

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)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors