3 citations · 4 across the 6 of their papers we have counts for
4 papers · 1 filter
A Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7
Hojoon Lee, Takuma Seno, Jun Jet Tai +4
Deep reinforcement learning has achieved superhuman racing performance in high-fidelity simulators like Gran Turismo 7 (GT7). It typically utilizes global features that require ins…
Hyperspherical Normalization for Scalable Deep Reinforcement Learning
Hojoon Lee, Youngdo Lee, Takuma Seno +3
Scaling up the model size and computation has brought consistent performance improvements in supervised learning. However, this lesson often fails to apply to reinforcement learnin…
SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning
Hojoon Lee, Dongyoon Hwang, Donghu Kim +7
Recent advances in CV and NLP have been largely driven by scaling up the number of network parameters, despite traditional theories suggesting that larger networks are prone to ove…
A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo
Miguel Vasco, Takuma Seno, Kenta Kawamoto +3
Racing autonomous cars faster than the best human drivers has been a longstanding grand challenge for the fields of Artificial Intelligence and robotics. Recently, an end-to-end de…