13 citations · 14 across the 7 of their papers we have counts for
3 papers · 1 filter
On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning
Giuseppe Canonaco, Leo Ardon, Alberto Pozanco +1
The use of Potential-Based Reward Shaping (PBRS) has shown great promise in the ongoing research effort to tackle sample inefficiency in Reinforcement Learning (RL). However, choos…
Time-Variant Variational Transfer for Value Functions
Giuseppe Canonaco, Andrea Soprani, Manuel Roveri +1
In most of the transfer learning approaches to reinforcement learning (RL) the distribution over the tasks is assumed to be stationary. Therefore, the target and source tasks are i…
Stochastic Variance-Reduced Policy Gradient
Matteo Papini, Damiano Binaghi, Giuseppe Canonaco +2
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 (MD…