Adam Stooke | DeepAI

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∙ 07/27/2021

Open-Ended Learning Leads to Generally Capable Agents

In this work we create agents that can perform well beyond a single, ind...

2 Open-Ended Learning Team, et al. ∙

∙ 09/14/2020

Decoupling Representation Learning from Reinforcement Learning

In an effort to overcome limitations of reward-driven feature learning i...

22 Adam Stooke, et al. ∙

∙ 07/08/2020

Responsive Safety in Reinforcement Learning by PID Lagrangian Methods

Lagrangian methods are widely used algorithms for constrained optimizati...

19 Adam Stooke, et al. ∙

∙ 06/26/2020

Perception-Prediction-Reaction Agents for Deep Reinforcement Learning

We introduce a new recurrent agent architecture and associated auxiliary...

5 Adam Stooke, et al. ∙

∙ 04/30/2020

Reinforcement Learning with Augmented Data

Learning from visual observations is a fundamental yet challenging probl...

14 Michael (Misha) Laskin, et al. ∙

∙ 09/03/2019

rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch

Since the recent advent of deep reinforcement learning for game play and...

532 Adam Stooke, et al. ∙

∙ 03/07/2018

Accelerated Methods for Deep Reinforcement Learning

Deep reinforcement learning (RL) has achieved many recent successes, yet...

0 Adam Stooke, et al. ∙

∙ 10/11/2017

Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

We present Synkhronos, an extension to Theano for multi-GPU computations...

0 Adam Stooke, et al. ∙

∙ 11/15/2016

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

Count-based exploration algorithms are known to perform near-optimally w...

0 Haoran Tang, et al. ∙

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