6 citations · 6 across the 4 of their papers we have counts for
4 papers
Safe Reinforcement Learning via Hierarchical Adaptive Chance-Constraint Safeguards
Zhaorun Chen, Zhuokai Zhao, Tairan He +4
Ensuring safety in Reinforcement Learning (RL), typically framed as a Constrained Markov Decision Process (CMDP), is crucial for real-world exploration applications. Current approa…
Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration
Zhaorun Chen, Binhao Chen, Shenghan Xie +4
In complex environments with high dimension, training a reinforcement learning (RL) model from scratch often suffers from lengthy and tedious collection of agent-environment intera…
POAR: Efficient Policy Optimization via Online Abstract State Representation Learning
Zhaorun Chen, Siqi Fan, Yuan Tan +6
While the rapid progress of deep learning fuels end-to-end reinforcement learning (RL), direct application, especially in high-dimensional space like robotic scenarios still suffer…
Exploration-efficient Deep Reinforcement Learning with Demonstration Guidance for Robot Control
Ke Lin, Liang Gong, Xudong Li +6
Although deep reinforcement learning (DRL) algorithms have made important achievements in many control tasks, they still suffer from the problems of sample inefficiency and unstabl…