47 citations · 264 across the 23 of their papers we have counts for
14 papers · 1 filter
State Regularized Policy Optimization on Data with Dynamics Shift
Zhenghai Xue, Qingpeng Cai, Shuchang Liu +4
In many real-world scenarios, Reinforcement Learning (RL) algorithms are trained on data with dynamics shift, i.e., with different underlying environment dynamics. A majority of cu…
Reinforcing User Retention in a Billion Scale Short Video Recommender System
Qingpeng Cai, Shuchang Liu, Xueliang Wang +6
Recently, short video platforms have achieved rapid user growth by recommending interesting content to users. The objective of the recommendation is to optimize user retention, the…
Two-Stage Constrained Actor-Critic for Short Video Recommendation
Qingpeng Cai, Zhenghai Xue, Chi Zhang +9
The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users sequentially intera…
Constrained Reinforcement Learning for Short Video Recommendation
Qingpeng Cai, Ruohan Zhan, Chi Zhang +5
The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users provide complex and…
A Survey on Deep Reinforcement Learning for Data Processing and Analytics
Qingpeng Cai, Can Cui, Yiyuan Xiong +3
Data processing and analytics are fundamental and pervasive. Algorithms play a vital role in data processing and analytics where many algorithm designs have incorporated heuristics…
Softmax Deep Double Deterministic Policy Gradients
Ling Pan, Qingpeng Cai, Longbo Huang
A widely-used actor-critic reinforcement learning algorithm for continuous control, Deep Deterministic Policy Gradients (DDPG), suffers from the overestimation problem, which can n…
Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-ranking in E-commerce
Jianxiong Wei, Anxiang Zeng, Yueqiu Wu +3
The slate re-ranking problem considers the mutual influences between items to improve user satisfaction in e-commerce, compared with the point-wise ranking. Previous works either d…
Multi-Path Policy Optimization
Ling Pan, Qingpeng Cai, Longbo Huang
Recent years have witnessed a tremendous improvement of deep reinforcement learning. However, a challenging problem is that an agent may suffer from inefficient exploration, partic…
Deterministic Value-Policy Gradients
Qingpeng Cai, Ling Pan, Pingzhong Tang
Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) has been widely used in continuous control tasks. However, the model-free DDPG alg…
Reinforcement Learning Driven Heuristic Optimization
Qingpeng Cai, Will Hang, Azalia Mirhoseini +3
Heuristic algorithms such as simulated annealing, Concorde, and METIS are effective and widely used approaches to find solutions to combinatorial optimization problems. However, th…
Reinforcement Learning with Dynamic Boltzmann Softmax Updates
Ling Pan, Qingpeng Cai, Qi Meng +3
Value function estimation is an important task in reinforcement learning, i.e., prediction. The Boltzmann softmax operator is a natural value estimator and can provide several bene…
Policy Optimization with Model-based Explorations
Feiyang Pan, Qingpeng Cai, An-Xiang Zeng +5
Model-free reinforcement learning methods such as the Proximal Policy Optimization algorithm (PPO) have successfully applied in complex decision-making problems such as Atari games…
Deterministic Policy Gradients With General State Transitions
Qingpeng Cai, Ling Pan, Pingzhong Tang
We study a reinforcement learning setting, where the state transition function is a convex combination of a stochastic continuous function and a deterministic function. Such a sett…
Policy Gradients for Contextual Recommendations
Feiyang Pan, Qingpeng Cai, Pingzhong Tang +2
Decision making is a challenging task in online recommender systems. The decision maker often needs to choose a contextual item at each step from a set of candidates. Contextual ba…