15 citations · 36 across the 7 of their papers we have counts for
6 papers
Safe Driving via Expert Guided Policy Optimization
Zhenghao Peng, Quanyi Li, Chunxiao Liu +1
When learning common skills like driving, beginners usually have domain experts standing by to ensure the safety of the learning process. We formulate such learning scheme under th…
Safe Exploration by Solving Early Terminated MDP
Hao Sun, Ziping Xu, Meng Fang +4
Safe exploration is crucial for the real-world application of reinforcement learning (RL). Previous works consider the safe exploration problem as Constrained Markov Decision Proce…
Improving the Generalization of End-to-End Driving through Procedural Generation
Quanyi Li, Zhenghao Peng, Qihang Zhang +2
Over the past few years there is a growing interest in the learning-based self driving system. To ensure safety, such systems are first developed and validated in simulators before…
Non-local Policy Optimization via Diversity-regularized Collaborative Exploration
Zhenghao Peng, Hao Sun, Bolei Zhou
Conventional Reinforcement Learning (RL) algorithms usually have one single agent learning to solve the task independently. As a result, the agent can only explore a limited part o…
AXNet: ApproXimate computing using an end-to-end trainable neural network
Zhenghao Peng, Xuyang Chen, Chengwen Xu +4
Neural network based approximate computing is a universal architecture promising to gain tremendous energy-efficiency for many error resilient applications. To guarantee the approx…
Approximate Random Dropout
Zhuoran Song, Ru Wang, Dongyu Ru +5
The training phases of Deep neural network~(DNN) consumes enormous processing time and energy. Compression techniques utilizing the sparsity of DNNs can effectively accelerate the…