2.3k citations · 3.8k across the 95 of their papers we have counts for
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Generalized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands
Thomas Kollar, Stefanie Tellex, Matthew Walter +8
Many task domains require robots to interpret and act upon natural language commands which are given by people and which refer to the robot's physical surroundings. Such interpreta…
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…
Sample Efficient Feature Selection for Factored MDPs
Zhaohan Daniel Guo, Emma Brunskill
In reinforcement learning, the state of the real world is often represented by feature vectors. However, not all of the features may be pertinent for solving the current task. We p…
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
Christoph Dann, Tor Lattimore, Emma Brunskill
Statistical performance bounds for reinforcement learning (RL) algorithms can be critical for high-stakes applications like healthcare. This paper introduces a new framework for th…