38 citations · 86 across the 16 of their papers we have counts for
16 papers
Off-OAB: Off-Policy Policy Gradient Method with Optimal Action-Dependent Baseline
Wenjia Meng, Qian Zheng, Long Yang +2
Policy-based methods have achieved remarkable success in solving challenging reinforcement learning problems. Among these methods, off-policy policy gradient methods are particular…
Policy Representation via Diffusion Probability Model for Reinforcement Learning
Long Yang, Zhixiong Huang, Fenghao Lei +6
Popular reinforcement learning (RL) algorithms tend to produce a unimodal policy distribution, which weakens the expressiveness of complicated policy and decays the ability of expl…
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…
Penalized Proximal Policy Optimization for Safe Reinforcement Learning
Linrui Zhang, Li Shen, Long Yang +4
Safe reinforcement learning aims to learn the optimal policy while satisfying safety constraints, which is essential in real-world applications. However, current algorithms still s…
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…