8 citations · 16 across the 4 of their papers we have counts for
9 papers
Towards A Unified Policy Abstraction Theory and Representation Learning Approach in Markov Decision Processes
Min Zhang, Hongyao Tang, Jianye Hao +1
Lying on the heart of intelligent decision-making systems, how policy is represented and optimized is a fundamental problem. The root challenge in this problem is the large scale a…
PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations
Tong Sang, Hongyao Tang, Yi Ma +5
Deep Reinforcement Learning (DRL) has been a promising solution to many complex decision-making problems. Nevertheless, the notorious weakness in generalization among environments…
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis
Yushi Cao, Zhiming Li, Tianpei Yang +5
Despite achieving superior performance in human-level control problems, unlike humans, deep reinforcement learning (DRL) lacks high-order intelligence (e.g., logic deduction and re…
Automatic Web Testing using Curiosity-Driven Reinforcement Learning
Yan Zheng, Yi Liu, Xiaofei Xie +4
Web testing has long been recognized as a notoriously difficult task. Even nowadays, web testing still heavily relies on manual efforts while automated web testing is far from achi…
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning
Jianwen Sun, Tianwei Zhang, Xiaofei Xie +4
Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed. However, the possibility and feasib…
KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge
Peng Zhang, Jianye Hao, Weixun Wang +4
Reinforcement learning agents usually learn from scratch, which requires a large number of interactions with the environment. This is quite different from the learning process of h…