1 citations · 5 across the 11 of their papers we have counts for
4 papers · 1 filter
-DQN: Improving Deep Q-Learning By Evolving the Behavior
Hongming Zhang, Fengshuo Bai, Chenjun Xiao +3
While many sophisticated exploration methods have been proposed, their lack of generality and high computational cost often lead researchers to favor simpler methods like -greed…
RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted Behaviors
Fengshuo Bai, Runze Liu, Yali Du +2
Evaluating deep reinforcement learning (DRL) agents against targeted behavior attacks is critical for assessing their robustness. These attacks aim to manipulate the victim into sp…
Efficient Preference-based Reinforcement Learning via Aligned Experience Estimation
Fengshuo Bai, Rui Zhao, Hongming Zhang +5
Preference-based reinforcement learning (PbRL) has shown impressive capabilities in training agents without reward engineering. However, a notable limitation of PbRL is its depende…
PEARL: Zero-shot Cross-task Preference Alignment and Robust Reward Learning for Robotic Manipulation
Runze Liu, Yali Du, Fengshuo Bai +2
In preference-based Reinforcement Learning (RL), obtaining a large number of preference labels are both time-consuming and costly. Furthermore, the queried human preferences cannot…