7 citations · 9 across the 6 of their papers we have counts for
9 papers · 1 filter
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…
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…
Thompson Sampling for Unimodal Bandits
Long Yang, Zhao Li, Zehong Hu +4
In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal} bandits, where the expected reward is unimodal over the partially ordered arms. To exploit the unimodal…
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…
Sample Complexity of Policy Gradient Finding Second-Order Stationary Points
Long Yang, Qian Zheng, Gang Pan
The goal of policy-based reinforcement learning (RL) is to search the maximal point of its objective. However, due to the inherent non-concavity of its objective, convergence to a…
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…