3.4k citations · 9.9k across the 108 of their papers we have counts for
8 papers · 1 filter
Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala +6
In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applicati…
Combining policy gradient and Q-learning
Brendan O'Donoghue, Remi Munos, Koray Kavukcuoglu +1
Policy gradient is an efficient technique for improving a policy in a reinforcement learning setting. However, vanilla online variants are on-policy only and not able to take advan…
Sample Efficient Actor-Critic with Experience Replay
Ziyu Wang, Victor Bapst, Nicolas Heess +4
This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environme…
Memory-Efficient Backpropagation Through Time
Audrūnas Gruslys, Remi Munos, Ivo Danihelka +2
We propose a novel approach to reduce memory consumption of the backpropagation through time (BPTT) algorithm when training recurrent neural networks (RNNs). Our approach uses dyna…
Successor Features for Transfer in Reinforcement Learning
André Barreto, Will Dabney, Rémi Munos +4
Transfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks. We propose a transfer framework for the scena…
Safe and Efficient Off-Policy Reinforcement Learning
Rémi Munos, Tom Stepleton, Anna Harutyunyan +1
In this work, we take a fresh look at some old and new algorithms for off-policy, return-based reinforcement learning. Expressing these in a common form, we derive a novel algorith…