4 citations · 23 across the 26 of their papers we have counts for
4 papers · 1 filter
State-wise Safe Reinforcement Learning: A Survey
Weiye Zhao, Tairan He, Rui Chen +2
Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation environments, applying RL to real-world applications still faces many challenges. A major con…
AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning
Tairan He, Weiye Zhao, Changliu Liu
Safety is a critical hurdle that limits the application of deep reinforcement learning (RL) to real-world control tasks. To this end, constrained reinforcement learning leverages c…
Reinforcement Learning with Automated Auxiliary Loss Search
Tairan He, Yuge Zhang, Kan Ren +5
A good state representation is crucial to solving complicated reinforcement learning (RL) challenges. Many recent works focus on designing auxiliary losses for learning informative…
Energy-Based Imitation Learning
Minghuan Liu, Tairan He, Minkai Xu +1
We tackle a common scenario in imitation learning (IL), where agents try to recover the optimal policy from expert demonstrations without further access to the expert or environmen…