45 citations · 215 across the 20 of their papers we have counts for
8 papers · 1 filter
Distilling Information from a Flood: A Possibility for the Use of Meta-Analysis and Systematic Review in Machine Learning Research
Peter Henderson, Emma Brunskill
The current flood of information in all areas of machine learning research, from computer vision to reinforcement learning, has made it difficult to make aggregate scientific infer…
Policy Certificates: Towards Accountable Reinforcement Learning
Christoph Dann, Lihong Li, Wei Wei +1
The performance of a reinforcement learning algorithm can vary drastically during learning because of exploration. Existing algorithms provide little information about the quality…
Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters
Aniruddh Raghu, Omer Gottesman, Yao Liu +4
In this work, we consider the problem of estimating a behaviour policy for use in Off-Policy Policy Evaluation (OPE) when the true behaviour policy is unknown. Via a series of empi…
Surprising Negative Results for Generative Adversarial Tree Search
Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu +2
While many recent advances in deep reinforcement learning (RL) rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsuper…
Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning
Ramtin Keramati, Jay Whang, Patrick Cho +1
Humans learn to play video games significantly faster than the state-of-the-art reinforcement learning (RL) algorithms. People seem to build simple models that are easy to learn to…
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms
Yao Liu, Emma Brunskill
Efficient exploration is one of the key challenges for reinforcement learning (RL) algorithms. Most traditional sample efficiency bounds require strategic exploration. Recently man…