45 citations · 85 across the 6 of their papers we have counts for
3 papers · 1 filter
On Ensuring that Intelligent Machines Are Well-Behaved
Philip S. Thomas, Bruno Castro da Silva, Andrew G. Barto +1
Machine learning algorithms are everywhere, ranging from simple data analysis and pattern recognition tools used across the sciences to complex systems that achieve super-human per…
Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines
Philip S. Thomas, Emma Brunskill
We show how an action-dependent baseline can be used by the policy gradient theorem using function approximation, originally presented with action-independent baselines by (Sutton…
Decoupling Learning Rules from Representations
Philip S. Thomas, Christoph Dann, Emma Brunskill
In the artificial intelligence field, learning often corresponds to changing the parameters of a parameterized function. A learning rule is an algorithm or mathematical expression…