3.4k citations · 9.9k across the 108 of their papers we have counts for
4 papers · 2 filters
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…
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…