322 citations · 877 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…