5 citations · 16 across the 8 of their papers we have counts for
8 papers
ConvFormer: Revisiting Transformer for Sequential User Modeling
Hao Wang, Jianxun Lian, Mingqi Wu +5
Sequential user modeling, a critical task in personalized recommender systems, focuses on predicting the next item a user would prefer, requiring a deep understanding of user behav…
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection
Jiajun Fan, Yuzheng Zhuang, Yuecheng Liu +5
The exploration problem is one of the main challenges in deep reinforcement learning (RL). Recent promising works tried to handle the problem with population-based methods, which c…
Generalized Data Distribution Iteration
Jiajun Fan, Changnan Xiao
To obtain higher sample efficiency and superior final performance simultaneously has been one of the major challenges for deep reinforcement learning (DRL). Previous work could han…
A Review for Deep Reinforcement Learning in Atari:Benchmarks, Challenges, and Solutions
Jiajun Fan
The Arcade Learning Environment (ALE) is proposed as an evaluation platform for empirically assessing the generality of agents across dozens of Atari 2600 games. ALE offers various…
GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning
Jiajun Fan, Changnan Xiao, Yue Huang
Deep Q Network (DQN) firstly kicked the door of deep reinforcement learning (DRL) via combining deep learning (DL) with reinforcement learning (RL), which has noticed that the dist…
An Entropy Regularization Free Mechanism for Policy-based Reinforcement Learning
Changnan Xiao, Haosen Shi, Jiajun Fan +1
Policy-based reinforcement learning methods suffer from the policy collapse problem. We find valued-based reinforcement learning methods with ε-greedy mechanism are capable of enjo…
CASA: Bridging the Gap between Policy Improvement and Policy Evaluation with Conflict Averse Policy Iteration
Changnan Xiao, Haosen Shi, Jiajun Fan +2
We study the problem of model-free reinforcement learning, which is often solved following the principle of Generalized Policy Iteration (GPI). While GPI is typically an interplay…
Critic PI2: Master Continuous Planning via Policy Improvement with Path Integrals and Deep Actor-Critic Reinforcement Learning
Jiajun Fan, He Ba, Xian Guo +1
Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods from AlphaGo to Muzer…