570 citations · 741 across the 5 of their papers we have counts for
5 papers
Noisy Networks for Exploration
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot +9
We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to…
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar +3
In this work we present a new agent architecture, called Reactor, which combines multiple algorithmic and architectural contributions to produce an agent with higher sample-efficie…
Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem
Masrour Zoghi, Shimon Whiteson, Remi Munos +1
This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms. O…
Risk-Aversion in Multi-armed Bandits
Amir Sani, Alessandro Lazaric, Rémi Munos
Stochastic multi-armed bandits solve the Exploration-Exploitation dilemma and ultimately maximize the expected reward. Nonetheless, in many practical problems, maximizing the expec…
Pure Exploration for Multi-Armed Bandit Problems
Sébastien Bubeck, Rémi Munos, Gilles Stoltz
We consider the framework of stochastic multi-armed bandit problems and study the possibilities and limitations of forecasters that perform an on-line exploration of the arms. Thes…