Search Results for author:
Found 5 papers, 3 papers with code
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.
Hyperparameter Selection for Imitation Learning
The vast literature in imitation learning mostly considers this reward function to be available for HP selection, but this is not a realistic setting.
*-CFQ: Analyzing the Scalability of Machine Learning on a Compositional Task
We present *-CFQ ("star-CFQ"): a suite of large-scale datasets of varying scope based on the CFQ semantic parsing benchmark, designed for principled investigation of the scalability of machine learning systems in a realistic compositional task setting.
Acme: A Research Framework for Distributed Reinforcement Learning
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
We present a large and realistic natural language question answering dataset that is constructed according to this method, and we use it to analyze the compositional generalization ability of three machine learning architectures.
