83 citations · 94 across the 8 of their papers we have counts for
8 papers
Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review
Chengmin Zhou, Chao Wang, Haseeb Hassan +3
Bayesian inference has many advantages in robotic motion planning over four perspectives: The uncertainty quantification of the policy, safety (risk-aware) and optimum guarantees o…
State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning
Chen Chen, Hongyao Tang, Yi Ma +4
Pessimism is of great importance in offline reinforcement learning (RL). One broad category of offline RL algorithms fulfills pessimism by explicit or implicit behavior regularizat…
Rethinking Reinforcement Learning based Logic Synthesis
Chao Wang, Chen Chen, Dong Li +1
Recently, reinforcement learning has been used to address logic synthesis by formulating the operator sequence optimization problem as a Markov decision process. However, through e…
Attacking and Defending Deep Reinforcement Learning Policies
Chao Wang
Recent studies have shown that deep reinforcement learning (DRL) policies are vulnerable to adversarial attacks, which raise concerns about applications of DRL to safety-critical s…
MOORe: Model-based Offline-to-Online Reinforcement Learning
Yihuan Mao, Chao Wang, Bin Wang +1
With the success of offline reinforcement learning (RL), offline trained RL policies have the potential to be further improved when deployed online. A smooth transfer of the policy…
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition
Hangyu Mao, Chao Wang, Xiaotian Hao +6
The MineRL competition is designed for the development of reinforcement learning and imitation learning algorithms that can efficiently leverage human demonstrations to drastically…