3 citations · 6 across the 2 of their papers we have counts for
2 papers
cs.RO2025★ 3 cited
AMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning
Lucas N. Alegre, Agon Serifi, Ruben Grandia +3
Reinforcement learning (RL) has significantly advanced the control of physics-based and robotic characters that track kinematic reference motion. However, methods typically rely on…
cs.LG2024★ 3 cited
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…