3 citations · 4 across the 7 of their papers we have counts for
7 papers
Coachable agents for interactive gameplay
Roberto Capobianco, Harm van Seijen, Nolan D. Bard +38
Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation m…
The Trajectory Alignment Coefficient in Two Acts: From Reward Tuning to Reward Learning
Calarina Muslimani, Yunshu Du, Kenta Kawamoto +3
The success of reinforcement learning (RL) is fundamentally tied to having a reward function that accurately reflects the task objective. Yet, designing reward functions is notorio…
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…
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…
Improving Wind Resistance Performance of Cascaded PID Controlled Quadcopters using Residual Reinforcement Learning
Yu Ishihara, Yuichi Hazama, Kousuke Suzuki +3
Wind resistance control is an essential feature for quadcopters to maintain their position to avoid deviation from target position and prevent collisions with obstacles. Convention…
Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning
Ce Hao, Catherine Weaver, Chen Tang +3
Hierarchical reinforcement learning (RL) can accelerate long-horizon decision-making by temporally abstracting a policy into multiple levels. Promising results in sparse reward env…
Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation
Ryuji Imamura, Takuma Seno, Kenta Kawamoto +1
When humans play virtual racing games, they use visual environmental information on the game screen to understand the rules within the environments. In contrast, a state-of-the-art…