21 citations · 81 across the 12 of their papers we have counts for
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cs.LG2021
Probability Density Estimation Based Imitation Learning
Yang Liu, Yongzhe Chang, Shilei Jiang +3
Imitation Learning (IL) is an effective learning paradigm exploiting the interactions between agents and environments. It does not require explicit reward signals and instead tries…
cs.LG2021★ 20 cited
Value Penalized Q-Learning for Recommender Systems
Chengqian Gao, Ke Xu, Kuangqi Zhou +4
Scaling reinforcement learning (RL) to recommender systems (RS) is promising since maximizing the expected cumulative rewards for RL agents meets the objective of RS, i.e., improvi…
cs.LG2021★ 18 cited
Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation
Tiantian Zhang, Xueqian Wang, Bin Liang +1
The powerful learning ability of deep neural networks enables reinforcement learning agents to learn competent control policies directly from continuous environments. In theory, to…