243 citations · 582 across the 7 of their papers we have counts for
3 papers · 1 filter
Reward learning from human preferences and demonstrations in Atari
Borja Ibarz, Jan Leike, Tobias Pohlen +3
To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions. Instead, we can have humans communicate an objective to the…
Neural Predictive Belief Representations
Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot +2
Unsupervised representation learning has succeeded with excellent results in many applications. It is an especially powerful tool to learn a good representation of environments wit…
Observe and Look Further: Achieving Consistent Performance on Atari
Tobias Pohlen, Bilal Piot, Todd Hester +10
Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks…