1.1k citations · 3.9k across the 19 of their papers we have counts for
8 papers · 1 filter
Universal Successor Features Approximators
Diana Borsa, André Barreto, John Quan +5
The ability of a reinforcement learning (RL) agent to learn about many reward functions at the same time has many potential benefits, such as the decomposition of complex tasks int…
Bayesian Optimization in AlphaGo
Yutian Chen, Aja Huang, Ziyu Wang +4
During the development of AlphaGo, its many hyper-parameters were tuned with Bayesian optimization multiple times. This automatic tuning process resulted in substantial improvement…
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning +15
Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based g…
Implicit Quantile Networks for Distributional Reinforcement Learning
Will Dabney, Georg Ostrovski, David Silver +1
In this work, we build on recent advances in distributional reinforcement learning to give a generally applicable, flexible, and state-of-the-art distributional variant of DQN. We…
Meta-Gradient Reinforcement Learning
Zhongwen Xu, Hado van Hasselt, David Silver
The goal of reinforcement learning algorithms is to estimate and/or optimise the value function. However, unlike supervised learning, no teacher or oracle is available to provide t…
Unsupervised Predictive Memory in a Goal-Directed Agent
Greg Wayne, Chia-Chun Hung, David Amos +21
Animals execute goal-directed behaviours despite the limited range and scope of their sensors. To cope, they explore environments and store memories maintaining estimates of import…