24 citations · 24 across the 6 of their papers we have counts for
6 papers
Highway Reinforcement Learning
Yuhui Wang, Miroslav Strupl, Francesco Faccio +5
Learning from multi-step off-policy data collected by a set of policies is a core problem of reinforcement learning (RL). Approaches based on importance sampling (IS) often suffer…
Contextual Conservative Q-Learning for Offline Reinforcement Learning
Ke Jiang, Jiayu Yao, Xiaoyang Tan
Offline reinforcement learning learns an effective policy on offline datasets without online interaction, and it attracts persistent research attention due to its potential of prac…
Robust Action Gap Increasing with Clipped Advantage Learning
Zhe Zhang, Yaozhong Gan, Xiaoyang Tan
Advantage Learning (AL) seeks to increase the action gap between the optimal action and its competitors, so as to improve the robustness to estimation errors. However, the method b…
Smoothing Advantage Learning
Yaozhong Gan, Zhe Zhang, Xiaoyang Tan
Advantage learning (AL) aims to improve the robustness of value-based reinforcement learning against estimation errors with action-gap-based regularization. Unfortunately, the meth…
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Chao Wen, Miao Xu, Zhilin Zhang +12
In online advertising, auto-bidding has become an essential tool for advertisers to optimize their preferred ad performance metrics by simply expressing high-level campaign objecti…
Greedy-Step Off-Policy Reinforcement Learning
Yuhui Wang, Qingyuan Wu, Pengcheng He +1
Most of the policy evaluation algorithms are based on the theories of Bellman Expectation and Optimality Equation, which derive two popular approaches - Policy Iteration (PI) and V…