2k citations · 3.7k across the 51 of their papers we have counts for
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Safe Reinforcement Learning for Emergency LoadShedding of Power Systems
Thanh Long Vu, Sayak Mukherjee, Tim Yin +3
The paradigm shift in the electric power grid necessitates a revisit of existing control methods to ensure the grid's security and resilience. In particular, the increased uncertai…
Learning Agile Locomotion Skills with a Mentor
Atil Iscen, George Yu, Alejandro Escontrela +3
Developing agile behaviors for legged robots remains a challenging problem. While deep reinforcement learning is a promising approach, learning truly agile behaviors typically requ…
Zero-Shot Terrain Generalization for Visual Locomotion Policies
Alejandro Escontrela, George Yu, Peng Xu +2
Legged robots have unparalleled mobility on unstructured terrains. However, it remains an open challenge to design locomotion controllers that can operate in a large variety of env…
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…
Learning Agile Locomotion via Adversarial Training
Yujin Tang, Jie Tan, Tatsuya Harada
Developing controllers for agile locomotion is a long-standing challenge for legged robots. Reinforcement learning (RL) and Evolution Strategy (ES) hold the promise of automating t…
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control
Renke Huang, Yujiao Chen, Tianzhixi Yin +6
Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability. With increased uncertainties and rapidly changing operati…
Learning Agile Robotic Locomotion Skills by Imitating Animals
Xue Bin Peng, Erwin Coumans, Tingnan Zhang +3
Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. While manually-designed controllers have been able to emulate many com…
Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous
Rose E. Wang, J. Chase Kew, Dennis Lee +5
Collaboration requires agents to align their goals on the fly. Underlying the human ability to align goals with other agents is their ability to predict the intentions of others an…
Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning
Xingyou Song, Yuxiang Yang, Krzysztof Choromanski +4
Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world. In this work, we present a new meta-learning method that allows…
Learning to Walk in the Real World with Minimal Human Effort
Sehoon Ha, Peng Xu, Zhenyu Tan +2
Reliable and stable locomotion has been one of the most fundamental challenges for legged robots. Deep reinforcement learning (deep RL) has emerged as a promising method for develo…