222 citations · 256 across the 3 of their papers we have counts for
5 papers
Active Reinforcement Learning: Observing Rewards at a Cost
David Krueger, Jan Leike, Owain Evans +1
Active reinforcement learning (ARL) is a variant on reinforcement learning where the agent does not observe the reward unless it chooses to pay a query cost c > 0. The central ques…
When Will AI Exceed Human Performance? Evidence from AI Experts
Katja Grace, John Salvatier, Allan Dafoe +2
Advances in artificial intelligence (AI) will transform modern life by reshaping transportation, health, science, finance, and the military. To adapt public policy, we need to bett…
Agent-Agnostic Human-in-the-Loop Reinforcement Learning
David Abel, John Salvatier, Andreas Stuhlmüller +1
Providing Reinforcement Learning agents with expert advice can dramatically improve various aspects of learning. Prior work has developed teaching protocols that enable agents to l…
Theano: A Python framework for fast computation of mathematical expressions
The Theano Development Team, Rami Al-Rfou, Guillaume Alain +110
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has bee…
Probabilistic Programming in Python using PyMC
John Salvatier, Thomas Wiecki, Christopher Fonnesbeck
Probabilistic programming (PP) allows flexible specification of Bayesian statistical models in code. PyMC3 is a new, open-source PP framework with an intutive and readable, yet pow…