43 citations · 81 across the 16 of their papers we have counts for
7 papers · 1 filter
When Maximum Entropy Misleads Policy Optimization
Ruipeng Zhang, Ya-Chien Chang, Sicun Gao
The Maximum Entropy Reinforcement Learning (MaxEnt RL) framework is a leading approach for achieving efficient learning and robust performance across many RL tasks. However, MaxEnt…
Improving Value Estimation Critically Enhances Vanilla Policy Gradient
Tao Wang, Ruipeng Zhang, Sicun Gao
Modern policy gradient algorithms, such as TRPO and PPO, outperform vanilla policy gradient in many RL tasks. Questioning the common belief that enforcing approximate trust regions…
Improving Compositional Generation with Diffusion Models Using Lift Scores
Chenning Yu, Sicun Gao
We introduce a novel resampling criterion using lift scores, for improving compositional generation in diffusion models. By leveraging the lift scores, we evaluate whether generate…
Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents
Chung-En Sun, Sicun Gao, Tsui-Wei Weng
Robustness remains a paramount concern in deep reinforcement learning (DRL), with randomized smoothing emerging as a key technique for enhancing this attribute. However, a notable…
Understanding the Difficulty of Solving Cauchy Problems with PINNs
Tao Wang, Bo Zhao, Sicun Gao +1
Physics-Informed Neural Networks (PINNs) have gained popularity in scientific computing in recent years. However, they often fail to achieve the same level of accuracy as classical…
Extremum-Seeking Action Selection for Accelerating Policy Optimization
Ya-Chien Chang, Sicun Gao
Reinforcement learning for control over continuous spaces typically uses high-entropy stochastic policies, such as Gaussian distributions, for local exploration and estimating poli…