26 citations · 55 across the 7 of their papers we have counts for
7 papers
DILI: A Distribution-Driven Learned Index (Extended version)
Pengfei Li, Hua Lu, Rong Zhu +3
Targeting in-memory one-dimensional search keys, we propose a novel DIstribution-driven Learned Index tree (DILI), where a concise and computation-efficient linear regression model…
Constrained Update Projection Approach to Safe Policy Optimization
Long Yang, Jiaming Ji, Juntao Dai +5
Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a…
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning
Long Yang, Jiaming Ji, Juntao Dai +3
Safe reinforcement learning (RL) is still very challenging since it requires the agent to consider both return maximization and safe exploration. In this paper, we propose CUP, a C…
On Convergence of Gradient Expected Sarsa()
Long Yang, Gang Zheng, Yu Zhang +3
We study the convergence of with linear function approximation. We show that applying the off-line estimate (multi-step bootstrapping) to $\mathtt{Expe…
Gradient Q: A Unified Algorithm with Function Approximation for Reinforcement Learning
Long Yang, Yu Zhang, Qian Zheng +2
Full-sampling (e.g., Q-learning) and pure-expectation (e.g., Expected Sarsa) algorithms are efficient and frequently used techniques in reinforcement learning. Q is the firs…
Policy Optimization with Stochastic Mirror Descent
Long Yang, Yu Zhang, Gang Zheng +5
Improving sample efficiency has been a longstanding goal in reinforcement learning. This paper proposes algorithm: a sample efficient policy gradient method with s…
Qualitative Measurements of Policy Discrepancy for Return-Based Deep Q-Network
Wenjia Meng, Qian Zheng, Long Yang +2
The deep Q-network (DQN) and return-based reinforcement learning are two promising algorithms proposed in recent years. DQN brings advances to complex sequential decision problems,…