1 citations · 1 across the 5 of their papers we have counts for
5 papers
Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey
Milan Ganai, Sicun Gao, Sylvia Herbert
Recent literature has proposed approaches that learn control policies with high performance while maintaining safety guarantees. Synthesizing Hamilton-Jacobi (HJ) reachable sets ha…
Safe and Reliable Training of Learning-Based Aerospace Controllers
Udayan Mandal, Guy Amir, Haoze Wu +10
In recent years, deep reinforcement learning (DRL) approaches have generated highly successful controllers for a myriad of complex domains. However, the opaque nature of these mode…
Iterative Reachability Estimation for Safe Reinforcement Learning
Milan Ganai, Zheng Gong, Chenning Yu +2
Ensuring safety is important for the practical deployment of reinforcement learning (RL). Various challenges must be addressed, such as handling stochasticity in the environments,…
Target-independent XLA optimization using Reinforcement Learning
Milan Ganai, Haichen Li, Theodore Enns +2
An important challenge in Machine Learning compilers like XLA is multi-pass optimization and analysis. There has been recent interest chiefly in XLA target-dependent optimization o…
Learning Stabilization Control from Observations by Learning Lyapunov-like Proxy Models
Milan Ganai, Chiaki Hirayama, Ya-Chien Chang +1
The deployment of Reinforcement Learning to robotics applications faces the difficulty of reward engineering. Therefore, approaches have focused on creating reward functions by Lea…