7 citations · 9 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
Expected Sarsa() with Control Variate for Variance Reduction
Long Yang, Yu Zhang, Jun Wen +3
Off-policy learning is powerful for reinforcement learning. However, the high variance of off-policy evaluation is a critical challenge, which causes off-policy learning falls into…
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…