3 citations · 4 across the 7 of their papers we have counts for
3 papers · 1 filter
CAMEL: Continuous Action Masking Enabled by Large Language Models for Reinforcement Learning
Yanxiao Zhao, Yangge Qian, Jingyang Shan +1
Reinforcement learning (RL) in continuous action spaces encounters persistent challenges, such as inefficient exploration and convergence to suboptimal solutions. To address these…
Snapshot Reinforcement Learning: Leveraging Prior Trajectories for Efficiency
Yanxiao Zhao, Yangge Qian, Tianyi Wang +2
Deep reinforcement learning (DRL) algorithms require substantial samples and computational resources to achieve higher performance, which restricts their practical application and…
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
Shengyi Huang, Quentin Gallouédec, Florian Felten +30
In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curv…