252 citations · 689 across the 37 of their papers we have counts for
8 papers · 1 filter
Datasets and Benchmarks for Offline Safe Reinforcement Learning
Zuxin Liu, Zijian Guo, Haohong Lin +8
This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation…
Constrained Decision Transformer for Offline Safe Reinforcement Learning
Zuxin Liu, Zijian Guo, Yihang Yao +4
Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the environment. We aim to tackle a more challenging problem: learning a safe policy fr…
Learning from Counterfactual Links for Link Prediction
Tong Zhao, Gang Liu, Daheng Wang +2
Learning to predict missing links is important for many graph-based applications. Existing methods were designed to learn the association between observed graph structure and exist…
Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning
Renke Huang, Yujiao Chen, Tianzhixi Yin +6
As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus,…
Learning Novel Policies For Tasks
Yunbo Zhang, Wenhao Yu, Greg Turk
In this work, we present a reinforcement learning algorithm that can find a variety of policies (novel policies) for a task that is given by a task reward function. Our method does…
Policy Transfer with Strategy Optimization
Wenhao Yu, C. Karen Liu, Greg Turk
Computer simulation provides an automatic and safe way for training robotic control policies to achieve complex tasks such as locomotion. However, a policy trained in simulation us…
Learning Symmetric and Low-energy Locomotion
Wenhao Yu, Greg Turk, C. Karen Liu
Learning locomotion skills is a challenging problem. To generate realistic and smooth locomotion, existing methods use motion capture, finite state machines or morphology-specific…
Preparing for the Unknown: Learning a Universal Policy with Online System Identification
Wenhao Yu, Jie Tan, C. Karen Liu +1
We present a new method of learning control policies that successfully operate under unknown dynamic models. We create such policies by leveraging a large number of training exampl…