1 citations · 1 across the 2 of their papers we have counts for
4 papers
HiL-ResRL: A Model-Agnostic Finetuning Adapter via Human-in-the-loop Residual Reinforcement Learning
Jingyi Liu, Zhaohong Mai, ShunSen He +5
Recent advancements in generative imitation learning have significantly propelled the field of robotic manipulation. However, the majority of existing models rely heavily on Behavi…
Visualizing Critic Match Loss Landscapes for Interpretation of Online Reinforcement Learning Control Algorithms
Jingyi Liu, Jian Guo, Eberhard Gill
Reinforcement learning has proven its power on various occasions. However, its performance is not always guaranteed when system dynamics change. Instead, it largely relies on users…
A Loss Landscape Visualization Framework for Interpreting Reinforcement Learning: An ADHDP Case Study
Jingyi Liu, Jian Guo, Eberhard Gill
Reinforcement learning algorithms have been widely used in dynamic and control systems. However, interpreting their internal learning behavior remains a challenge. In the authors'…
Adapting Critic Match Loss Landscape Visualization to Off-policy Reinforcement Learning
Jingyi Liu, Jian Guo, Eberhard Gill
This work extends an established critic match loss landscape visualization method from online to off-policy reinforcement learning (RL), aiming to reveal the optimization geometry…