2 papers
cs.RO2022
TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving
Qichao Zhang, Yinfeng Gao, Yikang Zhang +5
Realistic and diverse simulation scenarios with reactive and feasible agent behaviors can be used for validation and verification of self-driving system performance without relying…
cs.LG2021
Exploring More When It Needs in Deep Reinforcement Learning
Youtian Guo, Qi Gao
We propose a exploration mechanism of policy in Deep Reinforcement Learning, which is exploring more when agent needs, called Add Noise to Noise (AN2N). The core idea is: when the…