64 citations · 122 across the 9 of their papers we have counts for
3 papers · 2 filters
Efficient decorrelation of features using Gramian in Reinforcement Learning
Borislav Mavrin, Daniel Graves, Alan Chan
Learning good representations is a long standing problem in reinforcement learning (RL). One of the conventional ways to achieve this goal in the supervised setting is through regu…
Distributional Reinforcement Learning for Efficient Exploration
Borislav Mavrin, Shangtong Zhang, Hengshuai Yao +3
In distributional reinforcement learning (RL), the estimated distribution of value function models both the parametric and intrinsic uncertainties. We propose a novel and efficient…
Deep Reinforcement Learning with Decorrelation
Borislav Mavrin, Hengshuai Yao, Linglong Kong
Learning an effective representation for high-dimensional data is a challenging problem in reinforcement learning (RL). Deep reinforcement learning (DRL) such as Deep Q networks (D…