1 citations · 1 across the 3 of their papers we have counts for
5 papers
Hierarchical Meta-Reinforcement Learning via Automated Macro-Action Discovery
Minjae Cho, Chuangchuang Sun
Meta-Reinforcement Learning (Meta-RL) enables fast adaptation to new testing tasks. Despite recent advancements, it is still challenging to learn performant policies across multipl…
Sparsity-based Safety Conservatism for Constrained Offline Reinforcement Learning
Minjae Cho, Chuangchuang Sun
Reinforcement Learning (RL) has made notable success in decision-making fields like autonomous driving and robotic manipulation. Yet, its reliance on real-time feedback poses chall…
Constrained Meta-Reinforcement Learning for Adaptable Safety Guarantee with Differentiable Convex Programming
Minjae Cho, Chuangchuang Sun
Despite remarkable achievements in artificial intelligence, the deployability of learning-enabled systems in high-stakes real-world environments still faces persistent challenges.…
Distributionally Safe Reinforcement Learning under Model Uncertainty: A Single-Level Approach by Differentiable Convex Programming
Alaa Eddine Chriat, Chuangchuang Sun
Safety assurance is uncompromisable for safety-critical environments with the presence of drastic model uncertainties (e.g., distributional shift), especially with humans in the lo…
Wasserstein Distributionally Robust Control Barrier Function using Conditional Value-at-Risk with Differentiable Convex Programming
Alaa Eddine Chriat, Chuangchuang Sun
Control Barrier functions (CBFs) have attracted extensive attention for designing safe controllers for their deployment in real-world safety-critical systems. However, the percepti…