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Found 5 papers, 2 papers with code
Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning
While the native action space is completely intractable for simple DQN agents, our architecture can be used to establish an effective way to interact with different tasks, significantly improving the performance of the same DQN agent over different levels of abstraction.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Ranked #1 on
Abstract Algebra
on BIG-bench
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning
We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for recording, replaying, manipulating, annotating and sharing data in the context of Sequential Decision Making (SDM) including Reinforcement Learning (RL), Learning from Demonstrations, Offline RL or Imitation Learning.
The Option Keyboard: Combining Skills in Reinforcement Learning
Building on this insight and on previous results on transfer learning, we show how to approximate options whose cumulants are linear combinations of the cumulants of known options.
AndroidEnv: A Reinforcement Learning Platform for Android
We introduce AndroidEnv, an open-source platform for Reinforcement Learning (RL) research built on top of the Android ecosystem.
