49 citations · 71 across the 13 of their papers we have counts for
4 papers · 1 filter
Mind the Model, Not the Agent: The Primacy Bias in Model-based RL
Zhongjian Qiao, Jiafei Lyu, Xiu Li
The primacy bias in model-free reinforcement learning (MFRL), which refers to the agent's tendency to overfit early data and lose the ability to learn from new data, can significan…
Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation
Chang Meng, Chenhao Zhai, Yu Yang +2
Multi-behavior recommendation algorithms aim to leverage the multiplex interactions between users and items to learn users' latent preferences. Recent multi-behavior recommendation…
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…
Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse
Jiafei Lyu, Le Wan, Zongqing Lu +1
Sample efficiency is one of the most critical issues for online reinforcement learning (RL). Existing methods achieve higher sample efficiency by adopting model-based methods, Q-en…