research
∙
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...
share
research
∙
09/14/2020
Decoupling Representation Learning from Reinforcement Learning
In an effort to overcome limitations of reward-driven feature learning i...
research
∙
07/08/2020
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods
Lagrangian methods are widely used algorithms for constrained optimizati...
research
∙
06/26/2020
Perception-Prediction-Reaction Agents for Deep Reinforcement Learning
We introduce a new recurrent agent architecture and associated auxiliary...
research
∙
04/30/2020
Reinforcement Learning with Augmented Data
Learning from visual observations is a fundamental yet challenging probl...
research
∙
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...
research
∙
03/07/2018
Accelerated Methods for Deep Reinforcement Learning
Deep reinforcement learning (RL) has achieved many recent successes, yet...
research
∙
10/11/2017
Synkhronos: a Multi-GPU Theano Extension for Data Parallelism
We present Synkhronos, an extension to Theano for multi-GPU computations...
research
∙
11/15/2016
