2k citations · 3.7k across the 51 of their papers we have counts for
13 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…
Mnemosyne: Learning to Train Transformers with Transformers
Deepali Jain, Krzysztof Marcin Choromanski, Avinava Dubey +4
In this work, we propose a new class of learnable optimizers, called \textit{Mnemosyne}. It is based on the novel spatio-temporal low-rank implicit attention Transformers that can…
On the Robustness of Safe Reinforcement Learning under Observational Perturbations
Zuxin Liu, Zijian Guo, Zhepeng Cen +4
Safe reinforcement learning (RL) trains a policy to maximize the task reward while satisfying safety constraints. While prior works focus on the performance optimality, we find tha…
Evolving Pareto-Optimal Actor-Critic Algorithms for Generalizability and Stability
Juan Jose Garau-Luis, Yingjie Miao, John D. Co-Reyes +4
Generalizability and stability are two key objectives for operating reinforcement learning (RL) agents in the real world. Designing RL algorithms that optimize these objectives can…
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance
Michael Luo, Ashwin Balakrishna, Brijen Thananjeyan +6
Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments. Recent work learns risk measures which measure the probability of violating const…
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 to be Safe: Deep RL with a Safety Critic
Krishnan Srinivasan, Benjamin Eysenbach, Sehoon Ha +2
Safety is an essential component for deploying reinforcement learning (RL) algorithms in real-world scenarios, and is critical during the learning process itself. A natural first a…
Data Efficient Reinforcement Learning for Legged Robots
Yuxiang Yang, Ken Caluwaerts, Atil Iscen +3
We present a model-based framework for robot locomotion that achieves walking based on only 4.5 minutes (45,000 control steps) of data collected on a quadruped robot. To accurately…
Adaptive Power System Emergency Control using Deep Reinforcement Learning
Qiuhua Huang, Renke Huang, Weituo Hao +3
Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based…
NoRML: No-Reward Meta Learning
Yuxiang Yang, Ken Caluwaerts, Atil Iscen +2
Efficiently adapting to new environments and changes in dynamics is critical for agents to successfully operate in the real world. Reinforcement learning (RL) based approaches typi…
Soft Actor-Critic Algorithms and Applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen +8
Model-free deep reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks. However, these methods…
Learning to Walk via Deep Reinforcement Learning
Tuomas Haarnoja, Sehoon Ha, Aurick Zhou +3
Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of complex controllers that can map sensory inputs directly to low-level actions. In the domai…
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…