55 citations · 151 across the 5 of their papers we have counts for
7 papers
Open-Ended Learning Leads to Generally Capable Agents
Open Ended Learning Team, Adam Stooke, Anuj Mahajan +15
In this work we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behaviour to a massive, rich space of challenges. We…
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…
rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch
Adam Stooke, Pieter Abbeel
Since the recent advent of deep reinforcement learning for game play and simulated robotic control, a multitude of new algorithms have flourished. Most are model-free algorithms wh…
Accelerated Methods for Deep Reinforcement Learning
Adam Stooke, Pieter Abbeel
Deep reinforcement learning (RL) has achieved many recent successes, yet experiment turn-around time remains a key bottleneck in research and in practice. We investigate how to opt…