2 papers
cs.LG2026
Intrinsic Reward Policy Optimization for Sparse-Reward Environments
Minjae Cho, Huy Trong Tran
Exploration is essential in reinforcement learning as an agent relies on trial and error to learn an optimal policy. However, when rewards are sparse, naive exploration strategies,…
cs.LG2025
Contraction-Aware Reinforcement Learning for Nonlinear Control with Statistical Robustness
Minjae Cho, Hiroyasu Tsukamoto, Huy T. Tran +1
Control contraction metrics (CCMs)-defined by Riemannian metrics under which a closed-loop system is incrementally exponentially stable-offer a constructive framework for synthesiz…