322 citations · 877 across the 12 of their papers we have counts for
4 papers · 1 filter
Decoupling Representation Learning from Reinforcement Learning
Adam Stooke, Kimin Lee, Pieter Abbeel +1
In an effort to overcome limitations of reward-driven feature learning in deep reinforcement learning (RL) from images, we propose decoupling representation learning from policy le…
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods
Adam Stooke, Joshua Achiam, Pieter Abbeel
Lagrangian methods are widely used algorithms for constrained optimization problems, but their learning dynamics exhibit oscillations and overshoot which, when applied to safe rein…
Perception-Prediction-Reaction Agents for Deep Reinforcement Learning
Adam Stooke, Valentin Dalibard, Siddhant M. Jayakumar +2
We introduce a new recurrent agent architecture and associated auxiliary losses which improve reinforcement learning in partially observable tasks requiring long-term memory. We em…
Reinforcement Learning with Augmented Data
Michael Laskin, Kimin Lee, Adam Stooke +3
Learning from visual observations is a fundamental yet challenging problem in Reinforcement Learning (RL). Although algorithmic advances combined with convolutional neural networks…