A framework combining universal value function approximators with targeted training scenarios and data augmentation produces RL agents that adapt to user-specified styles in real time across video games and humanoid domains while preserving core task performance.
Scaling Up Multi-Task Robotic Reinforcement Learningin5th Annual Con- ference on Robot Learning(2021).https://openreview.net/forum?id=p9Pe-l9MMEq
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Coachable agents for interactive gameplay
A framework combining universal value function approximators with targeted training scenarios and data augmentation produces RL agents that adapt to user-specified styles in real time across video games and humanoid domains while preserving core task performance.