69 citations · 462 across the 66 of their papers we have counts for
10 papers · 1 filter
Dueling Posterior Sampling for Preference-Based Reinforcement Learning
Ellen R. Novoseller, Yibing Wei, Yanan Sui +2
In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback. While there is increasing researc…
Control Regularization for Reduced Variance Reinforcement Learning
Richard Cheng, Abhinav Verma, Gabor Orosz +3
Dealing with high variance is a significant challenge in model-free reinforcement learning (RL). Existing methods are unreliable, exhibiting high variance in performance from run t…
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks
Richard Cheng, Gabor Orosz, Richard M. Murray +1
Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process…
Stagewise Safe Bayesian Optimization with Gaussian Processes
Yanan Sui, Vincent Zhuang, Joel W. Burdick +1
Enforcing safety is a key aspect of many problems pertaining to sequential decision making under uncertainty, which require the decisions made at every step to be both informative…
A Function Approximation Method for Model-based High-Dimensional Inverse Reinforcement Learning
Kun Li, Joel W. Burdick
This works handles the inverse reinforcement learning problem in high-dimensional state spaces, which relies on an efficient solution of model-based high-dimensional reinforcement…
Inverse Reinforcement Learning in Large State Spaces via Function Approximation
Kun Li, Joel W. Burdick
This paper introduces a new method for inverse reinforcement learning in large-scale and high-dimensional state spaces. To avoid solving the computationally expensive reinforcement…
Bellman Gradient Iteration for Inverse Reinforcement Learning
Kun Li, Yanan Sui, Joel W. Burdick
This paper develops an inverse reinforcement learning algorithm aimed at recovering a reward function from the observed actions of an agent. We introduce a strategy to flexibly han…
Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces
Yanan Sui, Yisong Yue, Joel W. Burdick
We consider sequential decision making under uncertainty, where the goal is to optimize over a large decision space using noisy comparative feedback. This problem can be formulated…
Multi-dueling Bandits with Dependent Arms
Yanan Sui, Vincent Zhuang, Joel W. Burdick +1
The dueling bandits problem is an online learning framework for learning from pairwise preference feedback, and is particularly well-suited for modeling settings that elicit subjec…
Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization
Thomas Desautels, Andreas Krause, Joel Burdick
Can one parallelize complex exploration exploitation tradeoffs? As an example, consider the problem of optimal high-throughput experimental design, where we wish to sequentially de…