88 citations · 174 across the 10 of their papers we have counts for
5 papers · 1 filter
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning
Jie Ren, Yewen Li, Zihan Ding +2
Deep reinforcement learning (DRL) has successfully solved various problems recently, typically with a unimodal policy representation. However, grasping distinguishable skills for s…
Lyapunov-Based Reinforcement Learning State Estimator
Liang Hu, Chengwei Wu, Wei Pan
In this paper, we consider the state estimation problem for nonlinear stochastic discrete-time systems. We combine Lyapunov's method in control theory and deep reinforcement learni…
Model-free Reinforcement Learning with Robust Stability Guarantee
Minghao Han, Yuan Tian, Lixian Zhang +2
Reinforcement learning is showing great potentials in robotics applications, including autonomous driving, robot manipulation and locomotion. However, with complex uncertainties in…
BayesNAS: A Bayesian Approach for Neural Architecture Search
Hongpeng Zhou, Minghao Yang, Jun Wang +1
One-Shot Neural Architecture Search (NAS) is a promising method to significantly reduce search time without any separate training. It can be treated as a Network Compression proble…
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
Ying Wen, Yaodong Yang, Rui Luo +2
Humans are capable of attributing latent mental contents such as beliefs or intentions to others. The social skill is critical in daily life for reasoning about the potential conse…