65 citations · 251 across the 19 of their papers we have counts for
12 papers · 1 filter
A Survey of Constraint Formulations in Safe Reinforcement Learning
Akifumi Wachi, Xun Shen, Yanan Sui
Safety is critical when applying reinforcement learning (RL) to real-world problems. As a result, safe RL has emerged as a fundamental and powerful paradigm for optimizing an agent…
Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals
Chen Gong, Yue Chen, Yanan Sui +1
The detection of human sleep stages is widely used in the diagnosis and intervention of neurological and psychiatric diseases. Some patients with deep brain stimulator implanted co…
Safe Policy Optimization with Local Generalized Linear Function Approximations
Akifumi Wachi, Yunyue Wei, Yanan Sui
Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems. Existing safe exploration methods guaranteed safety under the assumption of regularity…
Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality
Songyuan Zhang, Zhangjie Cao, Dorsa Sadigh +1
Most existing imitation learning approaches assume the demonstrations are drawn from experts who are optimal, but relaxing this assumption enables us to use a wider range of data.…
No-Regret Reinforcement Learning with Heavy-Tailed Rewards
Vincent Zhuang, Yanan Sui
Reinforcement learning algorithms typically assume rewards to be sampled from light-tailed distributions, such as Gaussian or bounded. However, a wide variety of real-world systems…
Imitation with Neural Density Models
Kuno Kim, Akshat Jindal, Yang Song +3
We propose a new framework for Imitation Learning (IL) via density estimation of the expert's occupancy measure followed by Maximum Occupancy Entropy Reinforcement Learning (RL) us…
Safe Reinforcement Learning in Constrained Markov Decision Processes
Akifumi Wachi, Yanan Sui
Safe reinforcement learning has been a promising approach for optimizing the policy of an agent that operates in safety-critical applications. In this paper, we propose an algorith…
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…
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…
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…